H2O 3.46.0.2
REST API Reference
GET /3/About
Return information about this H2O cluster.
GET /3/Capabilities
List of all registered capabilities
GET /3/Capabilities/API
List of all registered Rest API capabilities
GET /3/Capabilities/Core
List registered core capabilities
GET /3/Cloud
Determine the status of the nodes in the H2O cloud.
HEAD /3/Cloud
Determine the status of the nodes in the H2O cloud.
POST /3/CloudLock
Lock the cloud.
GET /3/ComputeGram
Get weighted gram matrix
POST /3/CreateFrame
Create a synthetic H2O Frame with random data. You can specify the number of rows/columns, as well as column types: integer, real, boolean, time, string, categorical. The frame may also have a dedicated “response” column, and some of the entries in the dataset may be created as missing.
DELETE /3/DKV
Remove all keys from the H2O distributed K/V store.
DELETE /3/DKV/{key}
Remove an arbitrary key from the H2O distributed K/V store.
POST /3/DataInfoFrame
Test only
POST /3/DecryptionSetup
Install a decryption tool for parsing of encrypted data.
GET /3/DownloadDataset
Download dataset as a CSV.
GET /3/DownloadDataset.bin
Download dataset as a CSV.
POST /3/FeatureInteraction
Fetch feature interaction data
GET /3/Find
Find a value within a Frame.
GET /3/FrameChunks/{frame_id}
Return information about chunks for a given frame.
GET /3/Frames
Return all Frames in the H2O distributed K/V store.
DELETE /3/Frames
Delete all Frames from the H2O distributed K/V store.
POST /3/Frames/load
Load a frame from data on given path.
GET /3/Frames/{frame_id}
Return the specified Frame.
DELETE /3/Frames/{frame_id}
Delete the specified Frame from the H2O distributed K/V store.
GET /3/Frames/{frame_id}/columns
Return all the columns from a Frame.
GET /3/Frames/{frame_id}/columns/{column}
Return the specified column from a Frame.
GET /3/Frames/{frame_id}/columns/{column}/domain
Return the domains for the specified categorical column (“null” if the column is not a categorical).
GET /3/Frames/{frame_id}/columns/{column}/summary
Return the summary metrics for a column, e.g. min, max, mean, sigma, percentiles, etc.
POST /3/Frames/{frame_id}/export
Export a Frame to the given path with optional overwrite.
GET /3/Frames/{frame_id}/export/{path}/overwrite/{force}
[DEPRECATED] Export a Frame to the given path with optional overwrite.
GET /3/Frames/{frame_id}/light
Return a basic info about Frame to fill client Rapid expression cache.
POST /3/Frames/{frame_id}/save
Save frame data to the given path.
GET /3/Frames/{frame_id}/summary
Return a Frame, including the histograms, after forcing computation of rollups.
POST /3/FriedmansPopescusH
Fetch Friedman Popescus H.
POST /3/GarbageCollect
Explicitly call System.gc().
GET /3/GetGLMRegPath
Get full regularization path
POST /3/Grid.bin/import
Import previously saved grid model
POST /3/Grid.bin/{grid_id}/export
Export a Grid and its models.
GET /3/ImportFiles
[DEPRECATED] Import raw data files into a single-column H2O Frame.
POST /3/ImportFiles
Import raw data files into a single-column H2O Frame.
POST /3/ImportFilesMulti
Import raw data files from multiple directories (or different data sources) into a single-column H2O Frame.
POST /3/ImportHiveTable
Import Hive table into an H2O Frame.
GET /3/InitID
Issue a new session ID.
DELETE /3/InitID
End a session.
POST /3/Interaction
Create interactions between categorical columns.
GET /3/JStack
Report stack traces for all threads on all nodes.
GET /3/Jobs
Get a list of all the H2O Jobs (long-running actions).
GET /3/Jobs/{job_id}
Get the status of the given H2O Job (long-running action).
POST /3/Jobs/{job_id}/cancel
Cancel a running job.
GET /3/KillMinus3
Kill minus 3 on this node
POST /3/LogAndEcho
Save a message to the H2O logfile.
GET /3/Logs/nodes/{nodeidx}/files/{name}
Get named log file for a node.
POST /3/MakeGLMModel
Make a new GLM model based on existing one
GET /3/Metadata/endpoints
Return the list of (almost) all REST API endpoints.
GET /3/Metadata/endpoints/{path}
Return the REST API endpoint metadata, including documentation, for the endpoint specified by path or index.
GET /3/Metadata/schemaclasses/{classname}
Return the REST API schema metadata for specified schema class.
GET /3/Metadata/schemas
Return list of all REST API schemas.
GET /3/Metadata/schemas/{schemaname}
Return the REST API schema metadata for specified schema.
POST /3/MissingInserter
Insert missing values.
GET /3/ModelBuilders
Return the Model Builder metadata for all available algorithms.
POST /3/ModelBuilders/adaboost
Train a AdaBoost model.
POST /3/ModelBuilders/adaboost/parameters
Validate a set of AdaBoost model builder parameters.
POST /3/ModelBuilders/anovaglm
Train a ANOVAGLM model.
POST /3/ModelBuilders/anovaglm/parameters
Validate a set of ANOVAGLM model builder parameters.
POST /3/ModelBuilders/coxph
Train a CoxPH model.
POST /3/ModelBuilders/coxph/parameters
Validate a set of CoxPH model builder parameters.
POST /3/ModelBuilders/deeplearning
Train a DeepLearning model.
POST /3/ModelBuilders/deeplearning/parameters
Validate a set of DeepLearning model builder parameters.
POST /3/ModelBuilders/drf
Train a DRF model.
POST /3/ModelBuilders/drf/parameters
Validate a set of DRF model builder parameters.
POST /3/ModelBuilders/dt
Train a DT model.
POST /3/ModelBuilders/dt/parameters
Validate a set of DT model builder parameters.
POST /3/ModelBuilders/extendedisolationforest
Train a ExtendedIsolationForest model.
POST /3/ModelBuilders/extendedisolationforest/parameters
Validate a set of ExtendedIsolationForest model builder parameters.
POST /3/ModelBuilders/gam
Train a GAM model.
POST /3/ModelBuilders/gam/parameters
Validate a set of GAM model builder parameters.
POST /3/ModelBuilders/gbm
Train a GBM model.
POST /3/ModelBuilders/gbm/parameters
Validate a set of GBM model builder parameters.
POST /3/ModelBuilders/generic
Train a Generic model.
POST /3/ModelBuilders/generic/parameters
Validate a set of Generic model builder parameters.
POST /3/ModelBuilders/glm
Train a GLM model.
POST /3/ModelBuilders/glm/parameters
Validate a set of GLM model builder parameters.
POST /3/ModelBuilders/glrm
Train a GLRM model.
POST /3/ModelBuilders/glrm/parameters
Validate a set of GLRM model builder parameters.
POST /3/ModelBuilders/infogram
Train a Infogram model.
POST /3/ModelBuilders/infogram/parameters
Validate a set of Infogram model builder parameters.
POST /3/ModelBuilders/isolationforest
Train a IsolationForest model.
POST /3/ModelBuilders/isolationforest/parameters
Validate a set of IsolationForest model builder parameters.
POST /3/ModelBuilders/isotonicregression
Train a IsotonicRegression model.
POST /3/ModelBuilders/isotonicregression/parameters
Validate a set of IsotonicRegression model builder parameters.
POST /3/ModelBuilders/kmeans
Train a KMeans model.
POST /3/ModelBuilders/kmeans/parameters
Validate a set of KMeans model builder parameters.
POST /3/ModelBuilders/modelselection
Train a ModelSelection model.
POST /3/ModelBuilders/modelselection/parameters
Validate a set of ModelSelection model builder parameters.
POST /3/ModelBuilders/naivebayes
Train a NaiveBayes model.
POST /3/ModelBuilders/naivebayes/parameters
Validate a set of NaiveBayes model builder parameters.
POST /3/ModelBuilders/pca
Train a PCA model.
POST /3/ModelBuilders/pca/parameters
Validate a set of PCA model builder parameters.
POST /3/ModelBuilders/psvm
Train a PSVM model.
POST /3/ModelBuilders/psvm/parameters
Validate a set of PSVM model builder parameters.
POST /3/ModelBuilders/rulefit
Train a RuleFit model.
POST /3/ModelBuilders/rulefit/parameters
Validate a set of RuleFit model builder parameters.
POST /3/ModelBuilders/targetencoder
Train a TargetEncoder model.
POST /3/ModelBuilders/targetencoder/parameters
Validate a set of TargetEncoder model builder parameters.
POST /3/ModelBuilders/upliftdrf
Train a UpliftDRF model.
POST /3/ModelBuilders/upliftdrf/parameters
Validate a set of UpliftDRF model builder parameters.
POST /3/ModelBuilders/word2vec
Train a Word2Vec model.
POST /3/ModelBuilders/word2vec/parameters
Validate a set of Word2Vec model builder parameters.
POST /3/ModelBuilders/xgboost
Train a XGBoost model.
POST /3/ModelBuilders/xgboost/parameters
Validate a set of XGBoost model builder parameters.
GET /3/ModelBuilders/{algo}
Return the Model Builder metadata for the specified algorithm.
POST /3/ModelBuilders/{algo}/model_id
Return a new unique model_id for the specified algorithm.
GET /3/ModelMetrics
Return all the saved scoring metrics.
GET /3/ModelMetrics/frames/{frame}
Return the saved scoring metrics for the specified Frame.
GET /3/ModelMetrics/frames/{frame}/models/{model}
Return the saved scoring metrics for the specified Model and Frame.
DELETE /3/ModelMetrics/frames/{frame}/models/{model}
Return the saved scoring metrics for the specified Model and Frame.
GET /3/ModelMetrics/models/{model}
Return the saved scoring metrics for the specified Model.
GET /3/ModelMetrics/models/{model}/frames/{frame}
Return the saved scoring metrics for the specified Model and Frame.
DELETE /3/ModelMetrics/models/{model}/frames/{frame}
Return the saved scoring metrics for the specified Model and Frame.
POST /3/ModelMetrics/models/{model}/frames/{frame}
Return the scoring metrics for the specified Frame with the specified Model. If the Frame has already been scored with the Model then cached results will be returned; otherwise predictions for all rows in the Frame will be generated and the metrics will be returned.
POST /3/ModelMetrics/predictions_frame/{predictions_frame}/actuals_frame/{actuals_frame}
Create a ModelMetrics object from the predicted and actual values, and a domain for classification problems or a distribution family for regression problems.
GET /3/Models
Return all Models from the H2O distributed K/V store.
DELETE /3/Models
Delete all Models from the H2O distributed K/V store.
GET /3/Models.fetch.bin/{model_id}
Return the model in the binary format.
GET /3/Models.java/{model_id}
[DEPRECATED] Return the stream containing model implementation in Java code.
GET /3/Models.java/{model_id}/preview
Return potentially abridged model suitable for viewing in a browser (currently only used for java model code).
GET /3/Models/{model_id}
Return the specified Model from the H2O distributed K/V store, optionally with the list of compatible Frames.
DELETE /3/Models/{model_id}
Delete the specified Model from the H2O distributed K/V store.
GET /3/Models/{model_id}/mojo
Return the model in the MOJO format. This format can then be interpreted by gen_model.jar in order to perform prediction / scoring. Currently works for GBM and DRF algos only.
GET /3/NetworkTest
Run a network test to measure the performance of the cluster interconnect.
GET /3/NodePersistentStorage/categories/{category}/exists
Return true or false.
GET /3/NodePersistentStorage/categories/{category}/names/{name}/exists
Return true or false.
GET /3/NodePersistentStorage/configured
Return true or false.
POST /3/NodePersistentStorage/{category}
Store a value.
GET /3/NodePersistentStorage/{category}
Return all keys stored for a given category.
POST /3/NodePersistentStorage/{category}/{name}
Store a named value.
GET /3/NodePersistentStorage/{category}/{name}
Return value for a given name.
DELETE /3/NodePersistentStorage/{category}/{name}
Delete a key.
POST /3/Parse
Parse a raw byte-oriented Frame into a useful columnar data Frame.
POST /3/ParseSVMLight
Parse a raw byte-oriented Frame into a useful columnar data Frame.
POST /3/ParseSetup
Guess the parameters for parsing raw byte-oriented data into an H2O Frame.
POST /3/PartialDependence/
Create data for partial dependence plot(s) for the specified model and frame.
GET /3/PartialDependence/{name}
Fetch partial dependence data.
POST /3/PersistS3
Set Amazon S3 credentials (Secret Key ID, Secret Access Key)
DELETE /3/PersistS3
Remove store Amazon S3 credentials
GET /3/Ping
The endpoint used to let H2O know from external services that it should keep running.
POST /3/Predictions/models/{model}/frames/{frame}
Score (generate predictions) for the specified Frame with the specified Model. Both the Frame of predictions and the metrics will be returned.
GET /3/Profiler
Report real-time profiling information for all nodes (sorted, aggregated stack traces).
POST /3/Recovery/resume
Recover stored state and resume interrupted job.
POST /3/SaveToHiveTable
Save an H2O Frame contents into a Hive table.
POST /3/SegmentModelsBuilders/adaboost
Validate a set of AdaBoost model builder parameters.
POST /3/SegmentModelsBuilders/anovaglm
Validate a set of ANOVAGLM model builder parameters.
POST /3/SegmentModelsBuilders/coxph
Validate a set of CoxPH model builder parameters.
POST /3/SegmentModelsBuilders/deeplearning
Validate a set of DeepLearning model builder parameters.
POST /3/SegmentModelsBuilders/drf
Validate a set of DRF model builder parameters.
POST /3/SegmentModelsBuilders/dt
Validate a set of DT model builder parameters.
POST /3/SegmentModelsBuilders/extendedisolationforest
Validate a set of ExtendedIsolationForest model builder parameters.
POST /3/SegmentModelsBuilders/gam
Validate a set of GAM model builder parameters.
POST /3/SegmentModelsBuilders/gbm
Validate a set of GBM model builder parameters.
POST /3/SegmentModelsBuilders/generic
Validate a set of Generic model builder parameters.
POST /3/SegmentModelsBuilders/glm
Validate a set of GLM model builder parameters.
POST /3/SegmentModelsBuilders/glrm
Validate a set of GLRM model builder parameters.
POST /3/SegmentModelsBuilders/infogram
Validate a set of Infogram model builder parameters.
POST /3/SegmentModelsBuilders/isolationforest
Validate a set of IsolationForest model builder parameters.
POST /3/SegmentModelsBuilders/isotonicregression
Validate a set of IsotonicRegression model builder parameters.
POST /3/SegmentModelsBuilders/kmeans
Validate a set of KMeans model builder parameters.
POST /3/SegmentModelsBuilders/modelselection
Validate a set of ModelSelection model builder parameters.
POST /3/SegmentModelsBuilders/naivebayes
Validate a set of NaiveBayes model builder parameters.
POST /3/SegmentModelsBuilders/pca
Validate a set of PCA model builder parameters.
POST /3/SegmentModelsBuilders/psvm
Validate a set of PSVM model builder parameters.
POST /3/SegmentModelsBuilders/rulefit
Validate a set of RuleFit model builder parameters.
POST /3/SegmentModelsBuilders/targetencoder
Validate a set of TargetEncoder model builder parameters.
POST /3/SegmentModelsBuilders/upliftdrf
Validate a set of UpliftDRF model builder parameters.
POST /3/SegmentModelsBuilders/word2vec
Validate a set of Word2Vec model builder parameters.
POST /3/SegmentModelsBuilders/xgboost
Validate a set of XGBoost model builder parameters.
POST /3/SessionProperties
Set session property.
GET /3/SessionProperties
Get session property.
POST /3/Shutdown
Shut down the cluster.
POST /3/SignificantRules
Fetch significant rules table.
POST /3/SplitFrame
Split an H2O Frame.
GET /3/SteamMetrics
Get metrics for Steam from H2O.
GET /3/TargetEncoderTransform
Transform using give TargetEncoderModel
GET /3/Timeline
Debugging tool that provides information on current communication between nodes.
GET /3/Tree
Obtain a traverseable representation of a specific tree
GET /3/Typeahead/files
Typeahead hander for filename completion.
POST /3/UnlockKeys
Unlock all keys in the H2O distributed K/V store, to attempt to recover from a crash.
GET /3/WaterMeterCpuTicks/{nodeidx}
Return a CPU usage snapshot of all cores of all nodes in the H2O cluster.
GET /3/WaterMeterIo
Return IO usage snapshot of all nodes in the H2O cluster.
GET /3/WaterMeterIo/{nodeidx}
Return IO usage snapshot of all nodes in the H2O cluster.
GET /3/Word2VecSynonyms
Find synonyms using a word2vec model
GET /3/Word2VecTransform
Transform words to vectors using a word2vec model
POST /3/XGBoostExecutor.cleanup
Remote XGBoost execution - cleanup
POST /3/XGBoostExecutor.getBooster
Remote XGBoost execution - get booster
POST /3/XGBoostExecutor.getEvalMetric
Remote XGBoost execution - getEvalMetric
POST /3/XGBoostExecutor.init
Remote XGBoost execution - init
POST /3/XGBoostExecutor.setup
Remote XGBoost execution - setup
POST /3/XGBoostExecutor.update
Remote XGBoost execution - update
POST /4/Frames/$simple
Create frame with random (uniformly distributed) data. You can specify how many columns of each type to make; and what the desired range for each column type.
POST /4/Predictions/models/{model}/frames/{frame}
Score (generate predictions) for the specified Frame with the specified Model. Both the Frame of predictions and the metrics will be returned.
GET /4/endpoints
Returns the list of all REST API (v4) endpoints.
GET /4/jobs/{job_id}
Retrieve information about the current state of a job.
GET /4/modelsinfo
Return basic information about all models available to train.
POST /4/sessions
Start a new Rapids session, and return the session id.
DELETE /4/sessions/{session_key}
Close the Rapids session.
POST /99/Assembly
Fit an assembly to an input frame
GET /99/Assembly.fetch_mojo_pipeline/{assembly_id}/{file_name}
Generate a MOJO 2 pipeline artifact from the Assembly
GET /99/Assembly.java/{assembly_id}/{file_name}
Generate a Java POJO from the Assembly
GET /99/AutoML/{automl_id}
Fetch the specified AutoML object.
POST /99/AutoMLBuilder
Start an AutoML build process.
POST /99/DCTTransformer
Row-by-row discrete cosine transforms in 1D, 2D and 3D.
POST /99/Grid/adaboost
Run grid search for AdaBoost model.
POST /99/Grid/adaboost/resume
Resume grid search for AdaBoost model.
POST /99/Grid/aggregator
Run grid search for Aggregator model.
POST /99/Grid/aggregator/resume
Resume grid search for Aggregator model.
POST /99/Grid/anovaglm
Run grid search for ANOVAGLM model.
POST /99/Grid/anovaglm/resume
Resume grid search for ANOVAGLM model.
POST /99/Grid/coxph
Run grid search for CoxPH model.
POST /99/Grid/coxph/resume
Resume grid search for CoxPH model.
POST /99/Grid/deeplearning
Run grid search for DeepLearning model.
POST /99/Grid/deeplearning/resume
Resume grid search for DeepLearning model.
POST /99/Grid/drf
Run grid search for DRF model.
POST /99/Grid/drf/resume
Resume grid search for DRF model.
POST /99/Grid/dt
Run grid search for DT model.
POST /99/Grid/dt/resume
Resume grid search for DT model.
POST /99/Grid/extendedisolationforest
Run grid search for ExtendedIsolationForest model.
POST /99/Grid/extendedisolationforest/resume
Resume grid search for ExtendedIsolationForest model.
POST /99/Grid/gam
Run grid search for GAM model.
POST /99/Grid/gam/resume
Resume grid search for GAM model.
POST /99/Grid/gbm
Run grid search for GBM model.
POST /99/Grid/gbm/resume
Resume grid search for GBM model.
POST /99/Grid/generic
Run grid search for Generic model.
POST /99/Grid/generic/resume
Resume grid search for Generic model.
POST /99/Grid/glm
Run grid search for GLM model.
POST /99/Grid/glm/resume
Resume grid search for GLM model.
POST /99/Grid/glrm
Run grid search for GLRM model.
POST /99/Grid/glrm/resume
Resume grid search for GLRM model.
POST /99/Grid/infogram
Run grid search for Infogram model.
POST /99/Grid/infogram/resume
Resume grid search for Infogram model.
POST /99/Grid/isolationforest
Run grid search for IsolationForest model.
POST /99/Grid/isolationforest/resume
Resume grid search for IsolationForest model.
POST /99/Grid/isotonicregression
Run grid search for IsotonicRegression model.
POST /99/Grid/isotonicregression/resume
Resume grid search for IsotonicRegression model.
POST /99/Grid/kmeans
Run grid search for KMeans model.
POST /99/Grid/kmeans/resume
Resume grid search for KMeans model.
POST /99/Grid/modelselection
Run grid search for ModelSelection model.
POST /99/Grid/modelselection/resume
Resume grid search for ModelSelection model.
POST /99/Grid/naivebayes
Run grid search for NaiveBayes model.
POST /99/Grid/naivebayes/resume
Resume grid search for NaiveBayes model.
POST /99/Grid/pca
Run grid search for PCA model.
POST /99/Grid/pca/resume
Resume grid search for PCA model.
POST /99/Grid/psvm
Run grid search for PSVM model.
POST /99/Grid/psvm/resume
Resume grid search for PSVM model.
POST /99/Grid/rulefit
Run grid search for RuleFit model.
POST /99/Grid/rulefit/resume
Resume grid search for RuleFit model.
POST /99/Grid/stackedensemble
Run grid search for StackedEnsemble model.
POST /99/Grid/stackedensemble/resume
Resume grid search for StackedEnsemble model.
POST /99/Grid/svd
Run grid search for SVD model.
POST /99/Grid/svd/resume
Resume grid search for SVD model.
POST /99/Grid/targetencoder
Run grid search for TargetEncoder model.
POST /99/Grid/targetencoder/resume
Resume grid search for TargetEncoder model.
POST /99/Grid/upliftdrf
Run grid search for UpliftDRF model.
POST /99/Grid/upliftdrf/resume
Resume grid search for UpliftDRF model.
POST /99/Grid/word2vec
Run grid search for Word2Vec model.
POST /99/Grid/word2vec/resume
Resume grid search for Word2Vec model.
POST /99/Grid/xgboost
Run grid search for XGBoost model.
POST /99/Grid/xgboost/resume
Resume grid search for XGBoost model.
GET /99/Grids
Return all grids from H2O distributed K/V store.
GET /99/Grids/{grid_id}
Return the specified grid search result.
POST /99/ImportSQLTable
Import SQL table into an H2O Frame.
GET /99/Leaderboards
Return all the AutoML leaderboards.
GET /99/Leaderboards/{project_name}
Return the AutoML leaderboard for the given project.
POST /99/ModelBuilders/aggregator
Train a Aggregator model.
POST /99/ModelBuilders/aggregator/parameters
Validate a set of Aggregator model builder parameters.
POST /99/ModelBuilders/stackedensemble
Train a StackedEnsemble model.
POST /99/ModelBuilders/stackedensemble/parameters
Validate a set of StackedEnsemble model builder parameters.
POST /99/ModelBuilders/svd
Train a SVD model.
POST /99/ModelBuilders/svd/parameters
Validate a set of SVD model builder parameters.
POST /99/Models.bin/{model_id}
Import given binary model into H2O.
GET /99/Models.bin/{model_id}
Export given model.
GET /99/Models.mojo/{model_id}
Export given model as Mojo.
POST /99/Models.upload.bin/{model_id}
Upload given binary model into H2O.
GET /99/Models/{model_id}/json
Export given model details in json format.
POST /99/Rapids
Execute an Rapids AstRoot.
GET /99/Rapids/help
Produce help for Rapids AstRoot language.
GET /99/Sample
Example of an experimental endpoint. Call via /EXPERIMENTAL/Sample. Experimental endpoints can change at any moment.
POST /99/SegmentModelsBuilders/aggregator
Validate a set of Aggregator model builder parameters.
POST /99/SegmentModelsBuilders/stackedensemble
Validate a set of StackedEnsemble model builder parameters.
POST /99/SegmentModelsBuilders/svd
Validate a set of SVD model builder parameters.
POST /99/Tabulate
Tabulate one column vs another.
REST API Schema Reference
ANOVAGLMModelOutputV3
coefficients_table
TwoDimTable[] | Table of Coefficients | In |
transformed_columns_key
Key | AnovaGLM transformed predictor frame key. For debugging purposes only | In |
result_frame_key
Key | ANOVA table frame key containing Type III SS calculation, degree of freedom, F-statistics and p-values. This frame content is repeated in the model summary. | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
ANOVAGLMModelV3
model_id
Key | Model key | In/Out |
parameters
ANOVAGLMParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
ANOVAGLMModelOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
ANOVAGLMParametersV3
seed
long | Seed for pseudo random number generator (if applicable) | In |
standardize
boolean | Standardize numeric columns to have zero mean and unit variance | In |
family
enum | Family. Use binomial for classification with logistic regression, others are for regression problems. | In |
tweedie_variance_power
double | Tweedie variance power | In |
tweedie_link_power
double | Tweedie link power | In |
theta
double | Theta | In |
alpha
double[] | Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise. | In |
lambda
double[] | Regularization strength | In |
lambda_search
boolean | Use lambda search starting at lambda max, given lambda is then interpreted as lambda min | In |
solver
enum | AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. | In |
plug_values
Key | Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) | In |
non_negative
boolean | Restrict coefficients (not intercept) to be non-negative | In |
compute_p_values
boolean | Request p-values computation, p-values work only with IRLSM solver and no regularization | In |
max_iterations
int | Maximum number of iterations | In |
link
enum | Link function. | In |
prior
double | Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. | In |
highest_interaction_term
int | Limit the number of interaction terms, if 2 means interaction between 2 columns only, 3 for three columns and so on… Default to 2. | In |
type
int | Refer to the SS type 1, 2, 3, or 4. We are currently only supporting 3 | In |
early_stopping
boolean | Stop early when there is no more relative improvement on train or validation (if provided). | In |
save_transformed_framekeys
boolean | true to save the keys of transformed predictors and interaction column. | In |
nparallelism
int | Number of models to build in parallel. Default to 4. Adjust according to your system. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
missing_values_handling
enum | Handling of missing values. Either MeanImputation, Skip or PlugValues. | In/Out |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
ANOVAGLMV3
parameters
ANOVAGLMParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
AboutEntryV3
name
string | Property name | Out |
value
string | Property value | Out |
AboutV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
entries
Iced[] | List of properties about this running H2O instance | Out |
AdaBoostModelOutputV3
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
AdaBoostModelV3
model_id
Key | Model key | In/Out |
parameters
AdaBoostParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
AdaBoostOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
AdaBoostParametersV3
nlearners
int | Number of AdaBoost weak learners. | In |
weak_learner
enum | Choose a weak learner type. Defaults to AUTO, which means DRF. | In |
learn_rate
double | Learning rate (from 0.0 to 1.0) | In |
weak_learner_params
string | Customized parameters for the weak_learner algorithm. | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
AdaBoostV3
parameters
AdaBoostParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
AggregatorModelOutputV99
output_frame
Key | Aggregated Frame of Exemplars | In |
mapping_frame
Key | Aggregated Frame mapping to the rows in the original data | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
AggregatorModelV99
model_id
Key | Model key | In/Out |
parameters
AggregatorParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
AggregatorOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
AggregatorParametersV99
transform
enum | Transformation of training data | In |
pca_method
enum | Method for computing PCA (Caution: GLRM is currently experimental and unstable) | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
k
int | Rank of matrix approximation | In/Out |
max_iterations
int | Maximum number of iterations for PCA | In/Out |
target_num_exemplars
int | Targeted number of exemplars | In/Out |
rel_tol_num_exemplars
double | Relative tolerance for number of exemplars (e.g, 0.5 is +/- 50 percents) | In/Out |
seed
long | RNG seed for initialization | In/Out |
use_all_factor_levels
boolean | Whether first factor level is included in each categorical expansion | In/Out |
save_mapping_frame
boolean | Whether to export the mapping of the aggregated frame | In/Out |
num_iteration_without_new_exemplar
int | The number of iterations to run before aggregator exits if the number of exemplars collected didn’t change | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
AggregatorV99
parameters
AggregatorParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
AssemblyKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
AssemblyV99
steps
string[] | A list of steps describing the assembly line. | In |
frame
Key | Input Frame for the assembly. | In |
file_name
string | The name of the file (and generated class in case of pojo) | In |
assembly_id
string | The key of the Assembly object to retrieve from the DKV. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
result
Key | Output of the assembly line. | Out |
assembly
Key | A Key to the fit Assembly data structure | Out |
AutoMLBuildControlV99
stopping_criteria
AutoMLStoppingCriteria | Model performance based stopping criteria for the AutoML run. | In |
nfolds
int | Number of folds for k-fold cross-validation (defaults to -1 (AUTO), otherwise it must be >=2 or use 0 to disable). Disabling prevents Stacked Ensembles from being built. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (defaults to 5.0 and can be less than 1.0). Requires balance_classes. | In |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation predictions. This needs to be set to TRUE if running the same AutoML object for repeated runs because CV predictions are required to build additional Stacked Ensemble models in AutoML. | In |
keep_cross_validation_models
boolean | Whether to keep the cross-validated models. Keeping cross-validation models may consume significantly more memory in the H2O cluster. | In |
keep_cross_validation_fold_assignment
boolean | Whether to keep cross-validation assignments. | In |
export_checkpoints_dir
string | Path to a directory where every generated model will be stored. | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
project_name
string | Optional project name used to group models from multiple AutoML runs into a single Leaderboard; derived from the training data name if not specified. | In/Out |
distribution
enum | Distribution function used by algorithms that support it; other algorithms use their defaults. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
AutoMLBuildModelsV99
exclude_algos
enum[] | A list of algorithms to skip during the model-building phase. | In |
include_algos
enum[] | A list of algorithms to restrict to during the model-building phase. | In |
exploitation_ratio
double | The budget ratio (between 0 and 1) dedicated to the exploitation (vs exploration) phase. | In |
modeling_plan
StepDefinition[] | The list of modeling steps to be used by the AutoML engine (they may not all get executed, depending on other constraints). | In |
preprocessing
PreprocessingStepDefinition[] | The list of preprocessing steps to run. Only ‘target_encoding’ is currently supported. | In |
algo_parameters
Iced[] | Custom algorithm parameters. | In |
monotone_constraints
KeyValue[] | A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint. | In |
AutoMLBuildSpecV99
build_control
AutoMLBuildControl | Specification of overall controls for the AutoML build process. | In |
input_spec
AutoMLInput | Specification of the input data for the AutoML build process. | In |
build_models
AutoMLBuildModels | If present, specifies details of how to train models. | In |
job
Job | The AutoML Job key | Out |
AutoMLCustomParameterV99
scope
enum | Scope of application of the parameter (specific algo, or any algo). | In |
name
string | Name of the model parameter. | In |
value
Polymorphic | Value of the model parameter. | In |
AutoMLInputV99
training_frame
Key | ID of the training data frame. | In |
response_column
VecSpecifier | Response column | In |
validation_frame
Key | ID of the validation data frame (used for early stopping in grid searches and for early stopping of the AutoML process itself). | In |
blending_frame
Key | ID of the H2OFrame used to train the the metalearning algorithm in Stacked Ensembles (instead of relying on cross-validated predicted values). When provided, it is also recommended to disable cross validation by setting nfolds=0 and to provide a leaderboard frame for scoring purposes. | In |
leaderboard_frame
Key | ID of the leaderboard data frame (used to score models and rank them on the AutoML Leaderboard). | In |
fold_column
VecSpecifier | Fold column (contains fold IDs) in the training frame. These assignments are used to create the folds for cross-validation of the models. | In |
weights_column
VecSpecifier | Weights column in the training frame, which specifies the row weights used in model training. | In |
ignored_columns
string[] | Names of columns to ignore in the training frame when building models. | In |
sort_metric
enum | Metric used to sort leaderboard | In |
AutoMLKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
AutoMLStoppingCriteriaV99
seed
long | Seed for random number generator; set to a value other than -1 for reproducibility. | In |
max_models
int | Maximum number of models to build (optional). Always set this parameter to ensure AutoML reproducibility: all models are then trained until convergence and none is constrained by a time budget. | In |
max_runtime_secs
double | This argument specifies the maximum time that the AutoML process will run for. If both max_runtime_secs and max_models are specified, then the AutoML run will stop as soon as it hits either of these limits. If neither max_runtime_secs nor max_models are specified, then max_runtime_secs defaults to 3600 seconds (1 hour). | In |
max_runtime_secs_per_model
double | Maximum time to spend on each individual model (optional). Note that models constrained by a time budget are not guaranteed reproducible. | In |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression) | In |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In |
AutoMLV99
automl_id
Key | Optional AutoML run ID; omitting this returns all runs | In |
sort_metric
string | Metric used to sort leaderboard | In |
verbosity
enum | Verbosity level of the returned event log | In/Out |
project_name
string | Identifier for models that should be grouped together in the same leaderboard | In/Out |
training_frame
Key | ID of the actual training frame for this AutoML run after any automatic splitting | Out |
validation_frame
Key | ID of the actual validation frame for this AutoML run after any automatic splitting | Out |
blending_frame
Key | ID of the actual blending frame used to train the Stacked Ensembles in blending mode | Out |
leaderboard_frame
Key | ID of the actual leaderboard frame for this AutoML run after any automatic splitting | Out |
leaderboard
Leaderboard | The leaderboard for this project, potentially including models from other AutoML runs | Out |
leaderboard_table
TwoDimTable | The leaderboard for this project, potentially including models from other AutoML runs, for easy rendering | Out |
event_log
EventLog | Event log of this AutoML run | Out |
event_log_table
TwoDimTable | Event log of this AutoML run, for easy rendering | Out |
modeling_steps
StepDefinition[] | The list of modeling steps effectively used during the AutoML run | Out |
CapabilitiesV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
capabilities
Iced[] | List of H2O capabilities | Out |
CapabilityEntryV3
name
string | Extension name | Out |
CartesianSearchCriteriaV99
strategy
enum | Hyperparameter space search strategy. | In/Out |
CloudLockV3
reason
string | reason | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
CloudV3
skip_ticks
boolean | skip_ticks | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
version
string | version | Out |
branch_name
string | branch_name | Out |
last_commit_hash
string | last_commit_hash | Out |
describe
string | describe | Out |
compiled_by
string | compiled_by | Out |
compiled_on
string | compiled_on | Out |
build_number
string | build_number | Out |
build_age
string | build_age | Out |
build_too_old
boolean | build_too_old | Out |
node_idx
int | Node index number cloud status is collected from (zero-based) | Out |
cloud_name
string | cloud_name | Out |
cloud_size
int | cloud_size | Out |
cloud_uptime_millis
long | cloud_uptime_millis | Out |
cloud_internal_timezone
string | Cloud internal timezone | Out |
datafile_parser_timezone
string | Timezone used for parsing datetime columns | Out |
cloud_healthy
boolean | cloud_healthy | Out |
bad_nodes
int | Nodes reporting unhealthy | Out |
consensus
boolean | Cloud voting is stable | Out |
locked
boolean | Cloud is accepting new members or not | Out |
is_client
boolean | Cloud is in client mode. | Out |
nodes
Iced[] | nodes | Out |
internal_security_enabled
boolean | internal_security_enabled | Out |
leader_idx
int | leader_idx | Out |
ClusteringModelBuilderSchema
parameters
Parameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
ClusteringModelParametersSchemaV3
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
k
int | The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find up to k centroids. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
ColSpecifierV3
column_name
string | Name of the column | In/Out |
is_member_of_frames
string[] | List of fields which specify columns that must contain this column | In/Out |
ColV3
label
string | label | Out |
missing_count
long | missing | Out |
zero_count
long | zeros | Out |
positive_infinity_count
long | positive infinities | Out |
negative_infinity_count
long | negative infinities | Out |
mins
double[] | mins | Out |
maxs
double[] | maxs | Out |
mean
double | mean | Out |
sigma
double | sigma | Out |
type
string | datatype: {enum, string, int, real, time, uuid} | Out |
domain
string[] | domain; not-null for categorical columns only | Out |
domain_cardinality
int | cardinality of this column’s domain; not-null for categorical columns only | Out |
data
double[] | data | Out |
string_data
string[] | string data | Out |
precision
byte | decimal precision, -1 for all digits | Out |
histogram_bins
long[] | Histogram bins; null if not computed | Out |
histogram_base
double | Start of histogram bin zero | Out |
histogram_stride
double | Stride per bin | Out |
percentiles
double[] | Percentile values, matching the default percentiles | Out |
ColumnSpecsBase
name
string | Column Name | Out |
type
string | Column Type | Out |
format
string | Column Format (printf) | Out |
description
string | Column Description | Out |
ColumnsMappingV3
from
string[] | Input column(s) from the same encoding group. | In |
to
string[] | Output column(s) generated by the application of target encoding to the from group. | In |
ConfusionMatrixV3
CoxPHModelOutputV3
coefficients_table
TwoDimTable | Table of Coefficients | In |
var_coef
double[][] | var(coefficients) | In |
null_loglik
double | null log-likelihood | In |
loglik
double | log-likelihood | In |
loglik_test
double | log-likelihood test stat | In |
wald_test
double | Wald test stat | In |
score_test
double | Score test stat | In |
rsq
double | R-square | In |
maxrsq
double | Maximum R-square | In |
lre
double | log relative error | In |
iter
int | number of iterations | In |
x_mean_cat
double[][] | x weighted mean vector for categorical variables | In |
x_mean_num
double[][] | x weighted mean vector for numeric variables | In |
mean_offset
double[] | unweighted mean vector for numeric offsets | In |
offset_names
string[] | names of offsets | In |
n
long | n | In |
n_missing
long | number of rows with missing values | In |
total_event
long | total events | In |
time
double[] | time | In |
n_risk
double[] | number at risk | In |
n_event
double[] | number of events | In |
n_censor
double[] | number of censored obs | In |
cumhaz_0
double[] | baseline cumulative hazard | In |
var_cumhaz_1
double[] | component of var(cumhaz) | In |
var_cumhaz_2
Key | component of var(cumhaz) | In |
baseline_hazard
Key | Baseline Hazard | In |
baseline_survival
Key | Baseline Survival | In |
formula
string | formula | In |
ties
enum | ties | In |
concordance
double | concordance | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
CoxPHModelV3
model_id
Key | Model key | In/Out |
parameters
CoxPHParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
CoxPHOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
CoxPHParametersV3
interactions_only
string[] | A list of columns that should only be used to create interactions but should not itself participate in model training. | In |
interactions
string[] | A list of predictor column indices to interact. All pairwise combinations will be computed for the list. | In |
interaction_pairs
StringPair[] | A list of pairwise (first order) column interactions. | In |
use_all_factor_levels
boolean | (Internal. For development only!) Indicates whether to use all factor levels. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
start_column
VecSpecifier | Start Time Column. | In/Out |
stop_column
VecSpecifier | Stop Time Column. | In/Out |
stratify_by
string[] | List of columns to use for stratification. | In/Out |
ties
enum | Method for Handling Ties. | In/Out |
init
double | Coefficient starting value. | In/Out |
lre_min
double | Minimum log-relative error. | In/Out |
max_iterations
int | Maximum number of iterations. | In/Out |
single_node_mode
boolean | Run on a single node to reduce the effect of network overhead (for smaller datasets) | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
CoxPHV3
parameters
CoxPHParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
CreateFrameOriginalIV4
dest
Key | destination key | In |
rows
int | Number of rows | In |
cols
int | Number of data columns (in addition to the first response column) | In |
seed
long | Random number seed that determines the random values | In |
randomize
boolean | Whether frame should be randomized | In |
value
long | Constant value (for randomize=false) | In |
real_range
double | Range for real variables (-range … range) | In |
categorical_fraction
double | Fraction of categorical columns (for randomize=true) | In |
factors
int | Factor levels for categorical variables | In |
integer_fraction
double | Fraction of integer columns (for randomize=true) | In |
integer_range
int | Range for integer variables (-range … range) | In |
binary_fraction
double | Fraction of binary columns (for randomize=true) | In |
binary_ones_fraction
double | Fraction of 1’s in binary columns | In |
time_fraction
double | Fraction of date/time columns (for randomize=true) | In |
string_fraction
double | Fraction of string columns (for randomize=true) | In |
missing_fraction
double | Fraction of missing values | In |
has_response
boolean | Whether an additional response column should be generated | In |
response_factors
int | Number of factor levels of the first column (1=real, 2=binomial, N=multinomial) | In |
positive_response
boolean | For real-valued response variable: Whether the response should be positive only. | In |
_fields
string | Filter on the set of output fields: if you set _fields=”foo,bar,baz”, then only those fields will be included in the output; or you can specify _fields=”-goo,gee” to include all fields except goo and gee. If the result contains nested data structures, then you can refer to the fields within those structures as well. For example if you specify _fields=”foo(oof),bar(-rab)”, then only fields foo and bar will be included, and within foo there will be only field oof, whereas within bar all fields except rab will be reported. | In |
CreateFrameSimpleIV4
dest
Key | Id for the frame to be created. | In |
seed
long | Random number seed that determines the random values. | In |
nrows
int | Number of rows. | In |
ncols_real
int | Number of real-valued columns. Values in these columns will be uniformly distributed between real_lb and real_ub. | In |
ncols_int
int | Number of integer columns. | In |
ncols_enum
int | Number of enum (categorical) columns. | In |
ncols_bool
int | Number of boolean (binary) columns. | In |
ncols_str
int | Number of string columns. | In |
ncols_time
int | Number of time columns. | In |
real_lb
double | Lower bound for the range of the real-valued columns. | In |
real_ub
double | Upper bound for the range of the real-valued columns. | In |
int_lb
int | Lower bound for the range of integer columns. | In |
int_ub
int | Upper bound for the range of integer columns. | In |
enum_nlevels
int | Number of levels (categories) for the enum columns. | In |
bool_p
double | Fraction of ones in each boolean (binary) column. | In |
time_lb
long | Lower bound for the range of time columns (in ms since the epoch). | In |
time_ub
long | Upper bound for the range of time columns (in ms since the epoch). | In |
str_length
int | Length of generated strings in string columns. | In |
missing_fraction
double | Fraction of missing values. | In |
response_type
enum | Type of the response column to add. | In |
response_lb
double | Lower bound for the response variable (real/int/time types). | In |
response_ub
double | Upper bound for the response variable (real/int/time types). | In |
response_p
double | Frequency of 1s for the bool (binary) response column. | In |
response_nlevels
int | Number of categorical levels for the enum response column. | In |
_fields
string | Filter on the set of output fields: if you set _fields=”foo,bar,baz”, then only those fields will be included in the output; or you can specify _fields=”-goo,gee” to include all fields except goo and gee. If the result contains nested data structures, then you can refer to the fields within those structures as well. For example if you specify _fields=”foo(oof),bar(-rab)”, then only fields foo and bar will be included, and within foo there will be only field oof, whereas within bar all fields except rab will be reported. | In |
CreateFrameV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
dest
Key | destination key | In/Out |
rows
long | Number of rows | In/Out |
cols
int | Number of data columns (in addition to the first response column) | In/Out |
seed
long | Random number seed that determines the random values | In/Out |
seed_for_column_types
long | Random number seed for setting the column types | In/Out |
randomize
boolean | Whether frame should be randomized | In/Out |
value
long | Constant value (for randomize=false) | In/Out |
real_range
long | Range for real variables (-range … range) | In/Out |
categorical_fraction
double | Fraction of categorical columns (for randomize=true) | In/Out |
factors
int | Factor levels for categorical variables | In/Out |
integer_fraction
double | Fraction of integer columns (for randomize=true) | In/Out |
integer_range
long | Range for integer variables (-range … range) | In/Out |
binary_fraction
double | Fraction of binary columns (for randomize=true) | In/Out |
binary_ones_fraction
double | Fraction of 1’s in binary columns | In/Out |
time_fraction
double | Fraction of date/time columns (for randomize=true) | In/Out |
string_fraction
double | Fraction of string columns (for randomize=true) | In/Out |
missing_fraction
double | Fraction of missing values | In/Out |
has_response
boolean | Whether an additional response column should be generated | In/Out |
response_factors
int | Number of factor levels of the first column (1=real, 2=binomial, N=multinomial or ordinal) | In/Out |
positive_response
boolean | For real-valued response variable: Whether the response should be positive only. | In/Out |
key
Key | Job Key | Out |
DCTTransformerV3
dataset
Key | Dataset | In |
destination_frame
Key | Destination Frame ID | In |
dimensions
int[] | Dimensions of the input array: Height, Width, Depth (Nx1x1 for 1D, NxMx1 for 2D) | In |
inverse
boolean | Whether to do the inverse transform | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
DRFModelOutputV3
variable_importances
TwoDimTable | Variable Importances | Out |
init_f
double | The Intercept term, the initial model function value to which trees make adjustments | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
DRFModelV3
model_id
Key | Model key | In/Out |
parameters
DRFParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
DRFOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
DRFParametersV3
mtries
int | Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors | In |
binomial_double_trees
boolean | For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy. | In |
sample_rate
double | Row sample rate per tree (from 0.0 to 1.0) | In |
ntrees
int | Number of trees. | In |
max_depth
int | Maximum tree depth (0 for unlimited). | In |
min_rows
double | Fewest allowed (weighted) observations in a leaf. | In |
nbins
int | For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point | In |
nbins_top_level
int | For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level | In |
nbins_cats
int | For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. | In |
r2_stopping
double | r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
build_tree_one_node
boolean | Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. | In |
sample_rate_per_class
double[] | A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree | In |
col_sample_rate_per_tree
double | Column sample rate per tree (from 0.0 to 1.0) | In |
col_sample_rate_change_per_level
double | Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) | In |
score_tree_interval
int | Score the model after every so many trees. Disabled if set to 0. | In |
min_split_improvement
double | Minimum relative improvement in squared error reduction for a split to happen | In |
histogram_type
enum | What type of histogram to use for finding optimal split points | In |
calibrate_model
boolean | Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. | In |
in_training_checkpoints_dir
string | Create checkpoints into defined directory while training process is still running. In case of cluster shutdown, this checkpoint can be used to restart training. | In |
in_training_checkpoints_tree_interval
int | Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is defined | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
calibration_frame
Key | Data for model calibration | In/Out |
calibration_method
enum | Calibration method to use | In/Out |
check_constant_response
boolean | Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
DRFV3
parameters
DRFParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
DStackTraceV3
node
string | Node name | Out |
time
long | Unix epoch time | Out |
thread_traces
string[] | One trace per thread | Out |
DTModelOutputV3
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
DTModelV3
model_id
Key | Model key | In/Out |
parameters
DTParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
DTOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
DTParametersV3
max_depth
int | Max depth of tree. | In |
min_rows
int | Fewest allowed (weighted) observations in a leaf. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
seed
long | Seed for random numbers (affects sampling) | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
DTV3
parameters
DTParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
DataInfoFrameV3
frame
Key | input frame | In |
interactions
string[] | interactions | In |
use_all
boolean | use all factor levels | In |
standardize
boolean | standardize | In |
interactions_only
boolean | interactions only returned | In |
result
Key | output frame | Out |
DecryptionSetupV3
password
string | Key password | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
decrypt_tool_id
Key | Target key for the Decryption Tool | In/Out |
decrypt_impl
string | Implementation of the Decryption Tool | In/Out |
keystore_id
Key | Location of Java Keystore | In/Out |
keystore_type
string | Keystore type | In/Out |
key_alias
string | Key alias | In/Out |
cipher_spec
string | Specification of the cipher (and padding) | In/Out |
DecryptionToolKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
DeepLearningModelOutputV3
weights
Key[] | Frame keys for weight matrices | In |
biases
Key[] | Frame keys for bias vectors | In |
normmul
double[] | Normalization/Standardization multipliers for numeric predictors | Out |
normsub
double[] | Normalization/Standardization offsets for numeric predictors | Out |
normrespmul
double[] | Normalization/Standardization multipliers for numeric response | Out |
normrespsub
double[] | Normalization/Standardization offsets for numeric response | Out |
catoffsets
int[] | Categorical offsets for one-hot encoding | Out |
variable_importances
TwoDimTable | Variable Importances | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
DeepLearningModelV3
model_id
Key | Model key | In/Out |
parameters
DeepLearningParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
DeepLearningModelOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
DeepLearningParametersV3
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs. | In/Out |
activation
enum | Activation function. | In/Out |
hidden
int[] | Hidden layer sizes (e.g. [100, 100]). | In/Out |
epochs
double | How many times the dataset should be iterated (streamed), can be fractional. | In/Out |
train_samples_per_iteration
long | Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic. | In/Out |
target_ratio_comm_to_comp
double | Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning). | In/Out |
seed
long | Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded. | In/Out |
adaptive_rate
boolean | Adaptive learning rate. | In/Out |
rho
double | Adaptive learning rate time decay factor (similarity to prior updates). | In/Out |
epsilon
double | Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress). | In/Out |
rate
double | Learning rate (higher => less stable, lower => slower convergence). | In/Out |
rate_annealing
double | Learning rate annealing: rate / (1 + rate_annealing * samples). | In/Out |
rate_decay
double | Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1). | In/Out |
momentum_start
double | Initial momentum at the beginning of training (try 0.5). | In/Out |
momentum_ramp
double | Number of training samples for which momentum increases. | In/Out |
momentum_stable
double | Final momentum after the ramp is over (try 0.99). | In/Out |
nesterov_accelerated_gradient
boolean | Use Nesterov accelerated gradient (recommended). | In/Out |
input_dropout_ratio
double | Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). | In/Out |
hidden_dropout_ratios
double[] | Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5. | In/Out |
l1
double | L1 regularization (can add stability and improve generalization, causes many weights to become 0). | In/Out |
l2
double | L2 regularization (can add stability and improve generalization, causes many weights to be small. | In/Out |
max_w2
float | Constraint for squared sum of incoming weights per unit (e.g. for Rectifier). | In/Out |
initial_weight_distribution
enum | Initial weight distribution. | In/Out |
initial_weight_scale
double | Uniform: -value…value, Normal: stddev. | In/Out |
initial_weights
Key[] | A list of H2OFrame ids to initialize the weight matrices of this model with. | In/Out |
initial_biases
Key[] | A list of H2OFrame ids to initialize the bias vectors of this model with. | In/Out |
loss
enum | Loss function. | In/Out |
score_interval
double | Shortest time interval (in seconds) between model scoring. | In/Out |
score_training_samples
long | Number of training set samples for scoring (0 for all). | In/Out |
score_validation_samples
long | Number of validation set samples for scoring (0 for all). | In/Out |
score_duty_cycle
double | Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring). | In/Out |
classification_stop
double | Stopping criterion for classification error fraction on training data (-1 to disable). | In/Out |
regression_stop
double | Stopping criterion for regression error (MSE) on training data (-1 to disable). | In/Out |
quiet_mode
boolean | Enable quiet mode for less output to standard output. | In/Out |
score_validation_sampling
enum | Method used to sample validation dataset for scoring. | In/Out |
overwrite_with_best_model
boolean | If enabled, override the final model with the best model found during training. | In/Out |
autoencoder
boolean | Auto-Encoder. | In/Out |
use_all_factor_levels
boolean | Use all factor levels of categorical variables. Otherwise, the first factor level is omitted (without loss of accuracy). Useful for variable importances and auto-enabled for autoencoder. | In/Out |
standardize
boolean | If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data. | In/Out |
diagnostics
boolean | Enable diagnostics for hidden layers. | In/Out |
variable_importances
boolean | Compute variable importances for input features (Gedeon method) - can be slow for large networks. | In/Out |
fast_mode
boolean | Enable fast mode (minor approximation in back-propagation). | In/Out |
force_load_balance
boolean | Force extra load balancing to increase training speed for small datasets (to keep all cores busy). | In/Out |
replicate_training_data
boolean | Replicate the entire training dataset onto every node for faster training on small datasets. | In/Out |
single_node_mode
boolean | Run on a single node for fine-tuning of model parameters. | In/Out |
shuffle_training_data
boolean | Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is close to #nodes x #rows, of if using balance_classes). | In/Out |
missing_values_handling
enum | Handling of missing values. Either MeanImputation or Skip. | In/Out |
sparse
boolean | Sparse data handling (more efficient for data with lots of 0 values). | In/Out |
col_major
boolean | #DEPRECATED Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow down backpropagation. | In/Out |
average_activation
double | Average activation for sparse auto-encoder. #Experimental | In/Out |
sparsity_beta
double | Sparsity regularization. #Experimental | In/Out |
max_categorical_features
int | Max. number of categorical features, enforced via hashing. #Experimental | In/Out |
reproducible
boolean | Force reproducibility on small data (will be slow - only uses 1 thread). | In/Out |
export_weights_and_biases
boolean | Whether to export Neural Network weights and biases to H2O Frames. | In/Out |
mini_batch_size
int | Mini-batch size (smaller leads to better fit, larger can speed up and generalize better). | In/Out |
elastic_averaging
boolean | Elastic averaging between compute nodes can improve distributed model convergence. #Experimental | In/Out |
elastic_averaging_moving_rate
double | Elastic averaging moving rate (only if elastic averaging is enabled). | In/Out |
elastic_averaging_regularization
double | Elastic averaging regularization strength (only if elastic averaging is enabled). | In/Out |
pretrained_autoencoder
Key | Pretrained autoencoder model to initialize this model with. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
DeepLearningV3
parameters
DeepLearningParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
DownloadDataV3
frame_id
Key | Frame to download | In |
hex_string
boolean | Emit double values in a machine readable lossless format with Double.toHexString(). | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
csv
string | CSV Stream | Out |
filename
string | Suggested Filename | Out |
EndpointV4
url
string | Method+Url of the request; variable parts are enclosed in curly braces. For example: /4/schemas/{schema_name} | In |
description
string | Short description of the functionality provided by the endpoint. | In |
name
string | Unique name of the endpoint. These names can be used to look up the endpoint’s info via GET /4/endpoints/{name}. | In |
input_schema
string | Input schema. | In |
output_schema
string | Schema for the result returned by the endpoint. | In |
__schema
string | Url describing the schema of the current object. | In |
EndpointsListV4
endpoints
Route[] | List of endpoints in H2O REST API (v4). | In |
__schema
string | Url describing the schema of the current object. | In |
EventLogEntryV99
timestamp
long | Timestamp for this event, in milliseconds since Jan 1, 1970 | Out |
level
enum | Importance of this log event | Out |
stage
enum | Stage of the AutoML process for this log event | Out |
message
string | Message for this event | Out |
name
string | String identifier associated to this entry | Out |
value
string | Value associated to this entry | Out |
EventLogV99
automl_id
Key | ID of the AutoML run for which the event log was recorded | In/Out |
verbosity
enum | Verbosity level of the returned event log | In/Out |
events
EventLogEntry[] | List of events produced during the AutoML run | Out |
table
TwoDimTable | A table representation of this event log, for easy rendering | Out |
EventV3
date
string | Time when the event was recorded. Format is hh:mm:ss:ms | In |
nanos
long | Time in nanos | In |
type
enum | type of recorded event | In |
ExtendedIsolationForestModelOutputV3
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
ExtendedIsolationForestModelV3
model_id
Key | Model key | In/Out |
parameters
ExtendedIsolationForestParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
ExtendedIsolationForestOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
ExtendedIsolationForestParametersV3
ntrees
int | Number of Extended Isolation Forest trees. | In |
sample_size
int | Number of randomly sampled observations used to train each Extended Isolation Forest tree. | In |
extension_level
int | Maximum is N - 1 (N = numCols). Minimum is 0. Extended Isolation Forest with extension_Level = 0 behaves like Isolation Forest. | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
score_tree_interval
int | Score the model after every so many trees. Disabled if set to 0. | In |
disable_training_metrics
boolean | Disable calculating training metrics (expensive on large datasets) | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
ExtendedIsolationForestV3
parameters
ExtendedIsolationForestParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
FeatureInteractionV3
model_id
Key | Model id of interest | In |
max_interaction_depth
int | Maximum interaction depth | In |
max_tree_depth
int | Maximum tree depth | In |
max_deepening
int | Maximum deepening | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
feature_interaction
TwoDimTable[] | Feature importance table | Out |
FieldMetadataV3
schema_name
string | Schema name for this field, if it is_schema, or the name of the enum, if it’s an enum. | In |
name
string | Field name in the Schema | Out |
type
string | Type for this field | Out |
is_schema
boolean | Type for this field is itself a Schema. | Out |
value
Polymorphic | Value for this field | Out |
help
string | A short help description to appear alongside the field in a UI | Out |
label
string | The label that should be displayed for the field if the name is insufficient | Out |
required
boolean | Is this field required, or is the default value generally sufficient? | Out |
level
enum | How important is this field? The web UI uses the level to do a slow reveal of the parameters | Out |
direction
enum | Is this field an input, output or inout? | Out |
is_inherited
boolean | Is the field inherited from the parent schema? | Out |
inherited_from
string | If this field is inherited from a class higher in the hierarchy which one? | Out |
is_gridable
boolean | Is the field gridable (i.e., it can be used in grid call) | Out |
values
string[] | For enum-type fields the allowed values are specified using the values annotation; this is used in UIs to tell the user the allowed values, and for validation | Out |
json
boolean | Should this field be rendered in the JSON representation? | Out |
is_member_of_frames
string[] | For Vec-type fields this is the set of other Vec-type fields which must contain mutually exclusive values; for example, for a SupervisedModel the response_column must be mutually exclusive with the weights_column | Out |
is_mutually_exclusive_with
string[] | For Vec-type fields this is the set of Frame-type fields which must contain the named column; for example, for a SupervisedModel the response_column must be in both the training_frame and (if it’s set) the validation_frame | Out |
FindV3
key
Frame | Frame to search | In |
column
string | Column, or null for all | In |
row
long | Starting row for search | In |
match
string | Value to search for; leave blank for a search for missing values | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
prev
long | previous row with matching value, or -1 | Out |
next
long | next row with matching value, or -1 | Out |
FrameBaseV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
frame_id
Key | Frame ID | In/Out |
byte_size
long | Total data size in bytes | Out |
is_text
boolean | Is this Frame raw unparsed data? | Out |
FrameChunkV3
chunk_id
int | An identifier unique in scope of a given frame | Out |
row_count
int | Number of rows represented byt the chunk | Out |
node_idx
int | Index of H2O node where the chunk is located in | Out |
FrameChunksV3
frame_id
Key | ID of a given frame | In/Out |
chunks
Iced[] | Description of particular chunks | Out |
FrameKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
FrameLoadV3
frame_id
Key | Import frame under given key into DKV. | In |
dir
string | Source directory (hdfs, s3, local) containing serialized frame | In |
force
boolean | Override existing frame in case it exists or throw exception if set to false | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
job
Job | Job indicating progress | Out |
FrameSaveV3
frame_id
Key | Name of Frame of interest | In |
dir
string | Destination directory (hdfs, s3, local) | In |
force
boolean | Overwrite destination file in case it exists or throw exception if set to false. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
job
Job | Job indicating progress | Out |
FrameSynopsisV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
frame_id
Key | Frame ID | In/Out |
rows
long | Number of rows in the Frame | Out |
columns
long | Number of columns in the Frame | Out |
byte_size
long | Total data size in bytes | Out |
is_text
boolean | Is this Frame raw unparsed data? | Out |
FrameV3
row_offset
long | Row offset to display | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
row_count
int | Number of rows to display | In/Out |
column_offset
int | Column offset to return | In/Out |
column_count
int | Number of columns to return | In/Out |
full_column_count
int | Number of full columns to return. The columns between full_column_count and column_count will be returned without the data | In/Out |
total_column_count
int | Total number of columns in the Frame | In/Out |
frame_id
Key | Frame ID | In/Out |
checksum
long | checksum | Out |
rows
long | Number of rows in the Frame | Out |
num_columns
long | Number of columns in the Frame | Out |
default_percentiles
double[] | Default percentiles, from 0 to 1 | Out |
columns
Vec[] | Columns in the Frame | Out |
compatible_models
string[] | Compatible models, if requested | Out |
chunk_summary
TwoDimTable | Chunk summary | Out |
distribution_summary
TwoDimTable | Distribution summary | Out |
byte_size
long | Total data size in bytes | Out |
is_text
boolean | Is this Frame raw unparsed data? | Out |
FramesListV3
frame_id
Key | Name of Frame of interest | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
frames
Iced[] | Frames | Out |
FramesV3
frame_id
Key | Name of Frame of interest | In |
column
string | Name of column of interest | In |
find_compatible_models
boolean | Find and return compatible models? | In |
path
string | File output path | In |
force
boolean | Overwrite existing file | In |
num_parts
int | Number of part files to use (1=single file,-1=automatic) | In |
parallel
boolean | Use parallel export to a single file (doesn’t apply when num_parts != 1, creates temporary files in the destination directory) | In |
format
enum | Output file format. Defaults to ‘csv’. | In |
compression
string | Compression method (default none; gzip, bzip2, zstd and snappy available depending on runtime environment) | In |
write_checksum
boolean | Specifies if checksum should be written next to data files on export (if supported by export format). | In |
separator
byte | Field separator (default ‘,’) | In |
header
boolean | Use header (default true) | In |
quote_header
boolean | Quote column names in header line (default true) | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
row_offset
long | Row offset to return | In/Out |
row_count
int | Number of rows to return | In/Out |
column_offset
int | Column offset to return | In/Out |
full_column_count
int | Number of full columns to return. The columns between full_column_count and column_count will be returned without the data | In/Out |
column_count
int | Number of columns to return | In/Out |
job
Job | Job for export file | Out |
frames
Frame[] | Frames | Out |
compatible_models
Model[] | Compatible models | Out |
domain
string[][] | Domains | Out |
FriedmanPopescusHV3
model_id
Key | Model id of interest | In |
frame
Frame | Frame the model has been fitted to | In |
variables
string[] | Variables of interest | In |
h
double | Value of H statistic | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
GAMModelOutputV3
coefficients_table
TwoDimTable | Table of Coefficients | In |
coefficients_table_no_centering
TwoDimTable | Table of Coefficients without centering | In |
glm_scoring_history
TwoDimTable | GLM scoring history | In |
glm_model_summary
TwoDimTable | GLM model summary | In |
standardized_coefficient_magnitudes
TwoDimTable | Table of Standardized Coefficients Magnitudes | In |
gam_transformed_center_key
string | key storing gam columns and predictor columns. For debugging purposes only | In |
glm_zvalues
double[] | GLM Z values. For debugging purposes only | In |
glm_pvalues
double[] | GLM p values. For debugging purposes only | In |
glm_std_err
double[] | GLM standard error values. For debugging purposes only | In |
knot_locations
double[][] | knot locations for all gam columns. | In |
gam_knot_column_names
string[] | Gam column names for knots stored in knot_locations | In |
variable_importances
TwoDimTable | Variable Importances | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
GAMModelV3
model_id
Key | Model key | In/Out |
parameters
GAMParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
GAMModelOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
GAMParametersV3
seed
long | Seed for pseudo random number generator (if applicable) | In |
family
enum | Family. Use binomial for classification with logistic regression, others are for regression problems. | In |
tweedie_variance_power
double | Tweedie variance power | In |
tweedie_link_power
double | Tweedie link power | In |
theta
double | Theta | In |
solver
enum | AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. | In |
alpha
double[] | Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise. | In |
lambda
double[] | Regularization strength | In |
startval
double[] | double array to initialize coefficients for GAM. | In |
lambda_search
boolean | Use lambda search starting at lambda max, given lambda is then interpreted as lambda min | In |
early_stopping
boolean | Stop early when there is no more relative improvement on train or validation (if provided) | In |
nlambdas
int | Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. | In |
standardize
boolean | Standardize numeric columns to have zero mean and unit variance | In |
plug_values
Key | Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) | In |
non_negative
boolean | Restrict coefficients (not intercept) to be non-negative | In |
max_iterations
int | Maximum number of iterations | In |
beta_epsilon
double | Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver | In |
objective_epsilon
double | Converge if objective value changes less than this. Default indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. | In |
gradient_epsilon
double | Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. | In |
obj_reg
double | Likelihood divider in objective value computation, default is 1/nobs | In |
link
enum | Link function. | In |
intercept
boolean | Include constant term in the model | In |
prior
double | Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. | In |
cold_start
boolean | Only applicable to multiple alpha/lambda values when calling GLM from GAM. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch. | In |
lambda_min_ratio
double | Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. | In |
beta_constraints
Key | Beta constraints | In |
max_active_predictors
int | Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. | In |
interactions
string[] | A list of predictor column indices to interact. All pairwise combinations will be computed for the list. | In |
interaction_pairs
StringPair[] | A list of pairwise (first order) column interactions. | In |
compute_p_values
boolean | Request p-values computation, p-values work only with IRLSM solver and no regularization | In |
remove_collinear_columns
boolean | In case of linearly dependent columns, remove some of the dependent columns | In |
store_knot_locations
boolean | If set to true, will return knot locations as double[][] array for gam column names found knots_for_gam. Default to false. | In |
num_knots
int[] | Number of knots for gam predictors. If specified, must specify one for each gam predictor. For monotone I-splines, mininum = 2, for cs spline, minimum = 3. For thin plate, minimum is size of polynomial basis + 2. | In |
spline_orders
int[] | Order of I-splines or NBSplineTypeI M-splines used for gam predictors. If specified, must be the same size as gam_columns. For I-splines, the spline_orders will be the same as the polynomials used to generate the splines. For M-splines, the polynomials used to generate the splines will be spline_order-1. Values for bs=0 or 1 will be ignored. | In |
splines_non_negative
boolean[] | Valid for I-spline (bs=2) only. True if the I-splines are monotonically increasing (and monotonically non-decreasing) and False if the I-splines are monotonically decreasing (and monotonically non-increasing). If specified, must be the same size as gam_columns. Values for other spline types will be ignored. Default to true. | In |
gam_columns
string[][] | Arrays of predictor column names for gam for smoothers using single or multiple predictors like {{‘c1’},{‘c2’,’c3’},{‘c4’},…} | In |
scale
double[] | Smoothing parameter for gam predictors. If specified, must be of the same length as gam_columns | In |
bs
int[] | Basis function type for each gam predictors, 0 for cr, 1 for thin plate regression with knots, 2 for monotone I-splines, 3 for NBSplineTypeI M-splines (refer to doc here: https://github.com/h2oai/h2o-3/issues/6926). If specified, must be the same size as gam_columns | In |
keep_gam_cols
boolean | Save keys of model matrix | In |
standardize_tp_gam_cols
boolean | standardize tp (thin plate) predictor columns | In |
scale_tp_penalty_mat
boolean | Scale penalty matrix for tp (thin plate) smoothers as in R | In |
knot_ids
string[] | Array storing frame keys of knots. One for each gam column set specified in gam_columns | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
missing_values_handling
enum | Handling of missing values. Either MeanImputation, Skip or PlugValues. | In/Out |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
GAMV3
parameters
GAMParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
GBMModelOutputV3
variable_importances
TwoDimTable | Variable Importances | Out |
init_f
double | The Intercept term, the initial model function value to which trees make adjustments | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
GBMModelV3
model_id
Key | Model key | In/Out |
parameters
GBMParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
GBMOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
GBMParametersV3
learn_rate
double | Learning rate (from 0.0 to 1.0) | In |
learn_rate_annealing
double | Scale the learning rate by this factor after each tree (e.g., 0.99 or 0.999) | In |
sample_rate
double | Row sample rate per tree (from 0.0 to 1.0) | In |
col_sample_rate
double | Column sample rate (from 0.0 to 1.0) | In |
monotone_constraints
KeyValue[] | A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint. | In |
max_abs_leafnode_pred
double | Maximum absolute value of a leaf node prediction | In |
pred_noise_bandwidth
double | Bandwidth (sigma) of Gaussian multiplicative noise ~N(1,sigma) for tree node predictions | In |
interaction_constraints
string[][] | A set of allowed column interactions. | In |
auto_rebalance
boolean | Allow automatic rebalancing of training and validation datasets | In |
ntrees
int | Number of trees. | In |
max_depth
int | Maximum tree depth (0 for unlimited). | In |
min_rows
double | Fewest allowed (weighted) observations in a leaf. | In |
nbins
int | For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point | In |
nbins_top_level
int | For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level | In |
nbins_cats
int | For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. | In |
r2_stopping
double | r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
build_tree_one_node
boolean | Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. | In |
sample_rate_per_class
double[] | A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree | In |
col_sample_rate_per_tree
double | Column sample rate per tree (from 0.0 to 1.0) | In |
col_sample_rate_change_per_level
double | Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) | In |
score_tree_interval
int | Score the model after every so many trees. Disabled if set to 0. | In |
min_split_improvement
double | Minimum relative improvement in squared error reduction for a split to happen | In |
histogram_type
enum | What type of histogram to use for finding optimal split points | In |
calibrate_model
boolean | Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. | In |
in_training_checkpoints_dir
string | Create checkpoints into defined directory while training process is still running. In case of cluster shutdown, this checkpoint can be used to restart training. | In |
in_training_checkpoints_tree_interval
int | Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is defined | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
calibration_frame
Key | Data for model calibration | In/Out |
calibration_method
enum | Calibration method to use | In/Out |
check_constant_response
boolean | Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
GBMV3
parameters
GBMParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
GLMModelOutputV3
coefficients_table
TwoDimTable | Table of Coefficients | In |
random_coefficients_table
TwoDimTable | Table of Random Coefficients for HGLM | In |
coefficients_table_multinomials_with_class_names
TwoDimTable | Table of Coefficients with coefficients denoted with class names for GLM multinonimals only. | In |
standardized_coefficient_magnitudes
TwoDimTable | Standardized Coefficient Magnitudes | In |
lambda_best
double | Lambda minimizing the objective value, only applicable with lambda search or when arrays of alpha and lambdas are provided | In |
alpha_best
double | Alpha minimizing the objective value, only applicable when arrays of alphas are given | In |
best_submodel_index
int | submodel index minimizing the objective value, only applicable for arrays of alphas/lambda | In |
lambda_1se
double | Lambda best + 1 standard error. Only applicable with lambda search and cross-validation | In |
lambda_min
double | Minimum lambda value calculated that may be used for lambda search. Early-stop may happen and the minimum lambda value will not be used in this case. | In |
lambda_max
double | Starting lambda value used when lambda search is enabled. | In |
dispersion
double | Dispersion parameter, only applicable to Tweedie family (input/output) and fractional Binomial (output only) | In |
vif_predictor_names
string[] | Predictor names where variable inflation factors are calculated. | In |
coefficient_names
string[] | GLM model coefficients names. | In |
variable_inflation_factors
double[] | predictor variable inflation factors. | In |
linear_constraint_states
string[] | Beta (if exists) and linear constraints states | In |
linear_constraints_table
TwoDimTable | Table of beta (if exists) and linear constraints values and status | In |
regression_influence_diagnostics
Key | Contains the original dataset and the dfbetas calculated for each predictor. | In |
all_constraints_satisfied
boolean | True if all constraints conditions are satisfied. Otherwise, false. | In |
variable_importances
TwoDimTable | Variable Importances | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
GLMModelV3
model_id
Key | Model key | In/Out |
parameters
GLMParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
GLMOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
GLMParametersV3
seed
long | Seed for pseudo random number generator (if applicable). | In |
family
enum | Family. Use binomial for classification with logistic regression, others are for regression problems. | In |
rand_family
enum[] | Random Component Family array. One for each random component. Only support gaussian for now. | In |
tweedie_variance_power
double | Tweedie variance power | In |
dispersion_learning_rate
double | Dispersion learning rate is only valid for tweedie family dispersion parameter estimation using ml. It must be > 0. This controls how much the dispersion parameter estimate is to be changed when the calculated loglikelihood actually decreases with the new dispersion. In this case, instead of setting new dispersion = dispersion + change, we set new dispersion = dispersion + dispersion_learning_rate * change. Defaults to 0.5. | In |
tweedie_link_power
double | Tweedie link power. | In |
theta
double | Theta | In |
solver
enum | AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. | In |
alpha
double[] | Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise. | In |
lambda
double[] | Regularization strength | In |
lambda_search
boolean | Use lambda search starting at lambda max, given lambda is then interpreted as lambda min. | In |
early_stopping
boolean | Stop early when there is no more relative improvement on train or validation (if provided). | In |
nlambdas
int | Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. | In |
score_iteration_interval
int | Perform scoring for every score_iteration_interval iterations. | In |
standardize
boolean | Standardize numeric columns to have zero mean and unit variance. | In |
cold_start
boolean | Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch. | In |
influence
enum | If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. | In |
plug_values
Key | Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues). | In |
non_negative
boolean | Restrict coefficients (not intercept) to be non-negative. | In |
max_iterations
int | Maximum number of iterations. Value should >=1. A value of 0 is only set when only the model coefficient names and model coefficient dimensions are needed. | In |
beta_epsilon
double | Converge if beta changes less (using L-infinity norm) than beta esilon. ONLY applies to IRLSM solver. | In |
objective_epsilon
double | Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. | In |
gradient_epsilon
double | Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. | In |
obj_reg
double | Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs. | In |
link
enum | Link function. | In |
dispersion_parameter_method
enum | Method used to estimate the dispersion parameter for Tweedie, Gamma and Negative Binomial only. | In |
rand_link
enum[] | Link function array for random component in HGLM. | In |
startval
double[] | double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. If standardize is true, the standardized coefficients should be used. Otherwise, use the regular coefficients. | In |
random_columns
int[] | random columns indices for HGLM. | In |
calc_like
boolean | if true, will return likelihood function value. | In |
generate_variable_inflation_factors
boolean | if true, will generate variable inflation factors for numerical predictors. Default to false. | In |
intercept
boolean | Include constant term in the model | In |
build_null_model
boolean | If set, will build a model with only the intercept. Default to false. | In |
fix_dispersion_parameter
boolean | Only used for Tweedie, Gamma and Negative Binomial GLM. If set, will use the dispsersion parameter in init_dispersion_parameter as the standard error and use it to calculate the p-values. Default to false. | In |
init_dispersion_parameter
double | Only used for Tweedie, Gamma and Negative Binomial GLM. Store the initial value of dispersion parameter. If fix_dispersion_parameter is set, this value will be used in the calculation of p-values. | In |
HGLM
boolean | If set to true, will return HGLM model. Otherwise, normal GLM model will be returned. | In |
prior
double | Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. | In |
lambda_min_ratio
double | Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. | In |
beta_constraints
Key | Beta constraints | In |
linear_constraints
Key | Linear constraints: used to specify linear constraints involving more than one coefficients in standard form. It is only supported for solver IRLSM. It contains four columns: names (strings for coefficient names or constant), values, types ( strings of ‘Equal’ or ‘LessThanEqual’), constraint_numbers (0 for first linear constraint, 1 for second linear constraint, …). | In |
max_active_predictors
int | Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. | In |
interactions
string[] | A list of predictor column indices to interact. All pairwise combinations will be computed for the list. | In |
interaction_pairs
StringPair[] | A list of pairwise (first order) column interactions. | In |
compute_p_values
boolean | Request p-values computation, p-values work only with IRLSM solver. | In |
fix_tweedie_variance_power
boolean | If true, will fix tweedie variance power value to the value set in tweedie_variance_power. | In |
remove_collinear_columns
boolean | In case of linearly dependent columns, remove the dependent columns. | In |
generate_scoring_history
boolean | If set to true, will generate scoring history for GLM. This may significantly slow down the algo. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
missing_values_handling
enum | Handling of missing values. Either MeanImputation, Skip or PlugValues. | In/Out |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs. | In/Out |
dispersion_epsilon
double | If changes in dispersion parameter estimation or loglikelihood value is smaller than dispersion_epsilon, will break out of the dispersion parameter estimation loop using maximum likelihood. | In/Out |
tweedie_epsilon
double | In estimating tweedie dispersion parameter using maximum likelihood, this is used to choose the lower and upper indices in the approximating of the infinite series summation. | In/Out |
max_iterations_dispersion
int | Control the maximum number of iterations in the dispersion parameter estimation loop using maximum likelihood. | In/Out |
init_optimal_glm
boolean | If true, will initialize coefficients with values derived from GLM runs without linear constraints. Only available for linear constraints. | In/Out |
separate_linear_beta
boolean | If true, will keep the beta constraints and linear constraints separate. After new coefficients are found, first beta constraints will be applied followed by the application of linear constraints. Note that the beta constraints in this case will not be part of the objective function. If false, will combine the beta and linear constraints. | In/Out |
constraint_eta0
double | For constrained GLM only. It affects the setting of eta_k+1=eta_0/power(ck+1, alpha). | In/Out |
constraint_tau
double | For constrained GLM only. It affects the setting of c_k+1=tau*c_k. | In/Out |
constraint_alpha
double | For constrained GLM only. It affects the setting of eta_k = eta_0/pow(c_0, alpha). | In/Out |
constraint_beta
double | For constrained GLM only. It affects the setting of eta_k+1 = eta_k/pow(c_k, beta). | In/Out |
constraint_c0
double | For constrained GLM only. It affects the initial setting of epsilon_k = 1/c_0. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
GLMRegularizationPathV3
model
Key | source model | In |
lambdas
double[] | Computed lambda values | In |
alphas
double[] | alpha values used in building submodels | In |
explained_deviance_train
double[] | explained deviance on the training set | In |
explained_deviance_valid
double[] | explained deviance on the validation set | In |
coefficients
double[][] | coefficients for all lambdas | In |
coefficients_std
double[][] | standardized coefficients for all lambdas | In |
coefficient_names
string[] | coefficient names | In |
z_values
double[][] | z-values | In |
p_values
double[][] | p-values | In |
std_errs
double[][] | standard error | In |
GLMV3
parameters
GLMParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
GLRMModelOutputV3
iterations
int | Number of iterations executed | In |
updates
int | Number of updates executed | In |
objective
double | Current value of the objective function | In |
avg_change_obj
double | Average change in objective value on final iteration | In |
step_size
double | Final step size | In |
archetypes
TwoDimTable | Mapping from lower dimensional k-space to training features (Y) | In |
singular_vals
double[] | Singular values of XY matrix | In |
eigenvectors
TwoDimTable | Eigenvectors of XY matrix | In |
representation_name
string | Frame key name for X matrix | In |
importance
TwoDimTable | Standard deviation and importance of each principal component | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
GLRMModelV3
model_id
Key | Model key | In/Out |
parameters
GLRMParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
GLRMOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
GLRMParametersV3
transform
enum | Transformation of training data | In |
k
int | Rank of matrix approximation | In |
loss
enum | Numeric loss function | In |
multi_loss
enum | Categorical loss function | In |
loss_by_col
enum[] | Loss function by column (override) | In |
loss_by_col_idx
int[] | Loss function by column index (override) | In |
period
int | Length of period (only used with periodic loss function) | In |
regularization_x
enum | Regularization function for X matrix | In |
regularization_y
enum | Regularization function for Y matrix | In |
gamma_x
double | Regularization weight on X matrix | In |
gamma_y
double | Regularization weight on Y matrix | In |
max_iterations
int | Maximum number of iterations | In |
max_updates
int | Maximum number of updates, defaults to 2*max_iterations | In |
init_step_size
double | Initial step size | In |
min_step_size
double | Minimum step size | In |
seed
long | RNG seed for initialization | In |
init
enum | Initialization mode | In |
svd_method
enum | Method for computing SVD during initialization (Caution: Randomized is currently experimental and unstable) | In |
user_y
Key | User-specified initial Y | In |
user_x
Key | User-specified initial X | In |
loading_name
string | [Deprecated] Use representation_name instead. Frame key to save resulting X. | In |
representation_name
string | Frame key to save resulting X | In |
expand_user_y
boolean | Expand categorical columns in user-specified initial Y | In |
impute_original
boolean | Reconstruct original training data by reversing transform | In |
recover_svd
boolean | Recover singular values and eigenvectors of XY | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
GLRMV3
parameters
GLRMParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
GarbageCollectV3
(No fields)
GenericModelOutputV3
variable_importances
TwoDimTable | Variable Importances | Out |
original_model_identifier
string | Short identifier of the original algorithm name | Out |
original_model_full_name
string | Full, potentially long name of the original agorithm | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
GenericModelV3
model_id
Key | Model key | In/Out |
parameters
GenericModelParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
GenericModelOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
GenericParametersV3
path
string | Path to file with self-contained model archive. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_key
Key | Key to the self-contained model archive already uploaded to H2O. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
GenericV3
parameters
GenericModelParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
GramV3
X
Key | source data | In |
W
VecSpecifier | weight vector | In |
use_all_factor_levels
boolean | use all factor levels when doing 1-hot encoding | In |
standardize
boolean | standardize data | In |
skip_missing
boolean | skip missing values | In |
destination_frame
Key | Destination key for the resulting matrix. | Out |
GrepModelOutputV3
matches
string[] | Matching strings | In |
offsets
long[] | Byte offsets of matches | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
GrepModelV3
model_id
Key | Model key | In/Out |
parameters
GrepParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
GrepOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
GrepParametersV3
regex
string | regex | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
GrepV3
parameters
GrepParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
GridExportV3
grid_id
string | ID of the Grid to load from the directory | In |
grid_directory
string | Path to the directory with saved Grid search | In |
save_params_references
boolean | True if objects referenced by params should also be saved. | In |
export_cross_validation_predictions
boolean | Flag indicating whether the exported model artifacts should also include CV Holdout Frame predictions | In |
GridImportV3
grid_path
string | Full path to the file containing saved Grid | In |
load_params_references
boolean | If true will also load saved objects referenced by params. Will fail with an error if grid was saved without objects referenced by params. | In |
GridKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
GridSchemaV99
grid_id
Key | Grid id | In |
model_ids
Key[] | Model IDs built by a grid search | In |
sort_by
string | Model performance metric to sort by. Examples: logloss, residual_deviance, mse, rmse, mae,rmsle, auc, r2, f1, recall, precision, accuracy, mcc, err, err_count, lift_top_group, max_per_class_error | In/Out |
decreasing
boolean | Specify whether sort order should be decreasing. | In/Out |
hyper_names
string[] | Used hyper parameters. | Out |
failed_params
Parameters[] | List of failed parameters | Out |
warning_details
string[] | List of detailed warning messages | Out |
failure_details
string[] | List of detailed failure messages | Out |
failure_stack_traces
string[] | List of detailed failure stack traces | Out |
failed_raw_params
string[][] | List of raw parameters causing model building failure | Out |
training_metrics
ModelMetrics[] | Training model metrics for the returned models; only returned if sort_by is set | Out |
validation_metrics
ModelMetrics[] | Validation model metrics for the returned models; only returned if sort_by is set | Out |
cross_validation_metrics
ModelMetrics[] | Cross validation model metrics for the returned models; only returned if sort_by is set | Out |
cross_validation_metrics_summary
TwoDimTable[] | Cross validation model metrics summary for the returned models; only returned if sort_by is set | Out |
export_checkpoints_dir
string | Directory for Grid automatic checkpointing | Out |
summary_table
TwoDimTable | Summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
GridSearchSchema
parameters
Parameters | Basic model builder parameters. | In |
parallelism
int | Level of parallelism during grid model building. 1 = sequential building (default). 0 for adaptive parallelism. Any number > 1 sets the exact number of models built in parallel. | In |
recovery_dir
string | Path to a directory where grid will save everything necessary to resume training after cluster crash. | In |
job_id
Key | Key to use for the Job handling this GridSearch (internal use only). | In |
hyper_parameters
Map | Grid search parameters. | In/Out |
grid_id
Key | Destination id for this grid; auto-generated if not specified. | In/Out |
search_criteria
HyperSpaceSearchCriteria | Hyperparameter search criteria, including strategy and early stopping directives. If it is not given, exhaustive Cartesian is used. | In/Out |
total_models
int | Number of all models generated by grid search. | Out |
job
Job | Job Key. | Out |
GridsV99
H2OErrorV3
timestamp
long | Milliseconds since the epoch for the time that this H2OError instance was created. Generally this is a short time since the underlying error ocurred. | Out |
error_url
string | Error url | Out |
msg
string | Message intended for the end user (a data scientist). | Out |
dev_msg
string | Potentially more detailed message intended for a developer (e.g. a front end engineer or someone designing a language binding). | Out |
http_status
int | HTTP status code for this error. | Out |
values
Map | Any values that are relevant to reporting or handling this error. Examples are a key name if the error is on a key, or a field name and object name if it’s on a specific field. | Out |
exception_type
string | Exception type, if any. | Out |
exception_msg
string | Raw exception message, if any. | Out |
stacktrace
string[] | Stacktrace, if any. | Out |
H2OModelBuilderErrorV3
parameters
Parameters | Model builder parameters. | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
timestamp
long | Milliseconds since the epoch for the time that this H2OError instance was created. Generally this is a short time since the underlying error ocurred. | Out |
error_url
string | Error url | Out |
msg
string | Message intended for the end user (a data scientist). | Out |
dev_msg
string | Potentially more detailed message intended for a developer (e.g. a front end engineer or someone designing a language binding). | Out |
http_status
int | HTTP status code for this error. | Out |
values
Map | Any values that are relevant to reporting or handling this error. Examples are a key name if the error is on a key, or a field name and object name if it’s on a specific field. | Out |
exception_type
string | Exception type, if any. | Out |
exception_msg
string | Raw exception message, if any. | Out |
stacktrace
string[] | Stacktrace, if any. | Out |
HeartBeatEvent
sends
int | number of sent heartbeats | In |
recvs
int | number of received heartbeats | In |
date
string | Time when the event was recorded. Format is hh:mm:ss:ms | In |
nanos
long | Time in nanos | In |
type
enum | type of recorded event | In |
HyperSpaceSearchCriteriaV99
strategy
enum | Hyperparameter space search strategy. | In/Out |
IOEvent
io_flavor
string | flavor of the recorded io (ice/hdfs/…) | In |
node
string | node where this io event happened | In |
data
string | data info | In |
date
string | Time when the event was recorded. Format is hh:mm:ss:ms | In |
nanos
long | Time in nanos | In |
type
enum | type of recorded event | In |
ImportFilesMultiV3
paths
string[] | paths | In |
pattern
string | pattern | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
files
string[] | files | Out |
destination_frames
string[] | names | Out |
fails
string[] | fails | Out |
dels
string[] | dels | Out |
ImportFilesV3
path
string | path | In |
pattern
string | pattern | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
files
string[] | files | Out |
destination_frames
string[] | names | Out |
fails
string[] | fails | Out |
dels
string[] | dels | Out |
ImportHiveTableV3
database
string | database | In |
table
string | table | In |
partitions
string[][] | partitions | In |
allow_multi_format
boolean | partitions | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
ImportSQLTableV99
connection_url
string | connection_url | In |
table
string | table | In |
select_query
string | select_query | In |
use_temp_table
string | use_temp_table | In |
temp_table_name
string | temp_table_name | In |
username
string | username | In |
password
string | password | In |
columns
string | columns | In |
fetch_mode
string | Mode for data loading. All modes may not be supported by all databases. | In |
num_chunks_hint
string | Desired number of chunks for the target Frame. Optional. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
InfogramModelOutputV3
admissible_cmi
double[] | Array of conditional mutual information for admissible features normalized to 0.0 and 1.0 | Out |
admissible_cmi_raw
double[] | Array of conditional mutual information for admissible features raw and not normalized to 0.0 and 1.0 | Out |
admissible_relevance
double[] | Array of variable importance for admissible features | Out |
admissible_features
string[] | Array containing names of admissible features for the user | Out |
admissible_features_valid
string[] | Array containing names of admissible features for the user from the validation dataset. | Out |
admissible_features_xval
string[] | Array containing names of admissible features for the user from cross-validation. | Out |
cmi_raw
double[] | Array of raw conditional mutual information for all features excluding sensitive attributes if applicable | Out |
cmi
double[] | Array of conditional mutual information for all features excluding sensitive attributes if applicable normalized to 0.0 and 1.0 | Out |
all_predictor_names
string[] | Array containing names of all features excluding sensitive attributes if applicable corresponding to CMI and relevance | Out |
relevance
double[] | Array of variable importance for all features excluding sensitive attributes if applicable | Out |
admissible_score_key
Key | Frame key that stores the predictor names, net CMI and relevance. | Out |
admissible_score_key_valid
Key | Frame key that stores the predictor names, net CMI and relevance calculated from validation dataset. | Out |
admissible_score_key_xval
Key | Frame key that stores the predictor names, net CMI and relevance from cross-validation. | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
InfogramModelV3
model_id
Key | Model key | In/Out |
parameters
InfogramParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
InfogramModelOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
InfogramParametersV3
seed
long | Seed for pseudo random number generator (if applicable). | In |
standardize
boolean | Standardize numeric columns to have zero mean and unit variance. | In |
plug_values
Key | Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues). | In |
max_iterations
int | Maximum number of iterations. | In |
prior
double | Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. | In |
algorithm_params
string | Customized parameters for the machine learning algorithm specified in the algorithm parameter. | In |
protected_columns
string[] | Columns that contain features that are sensitive and need to be protected (legally, or otherwise), if applicable. These features (e.g. race, gender, etc) should not drive the prediction of the response. | In |
total_information_threshold
double | A number between 0 and 1 representing a threshold for total information, defaulting to 0.1. For a specific feature, if the total information is higher than this threshold, and the corresponding net information is also higher than the threshold net_information_threshold, that feature will be considered admissible. The total information is the x-axis of the Core Infogram. Default is -1 which gets set to 0.1. | In |
net_information_threshold
double | A number between 0 and 1 representing a threshold for net information, defaulting to 0.1. For a specific feature, if the net information is higher than this threshold, and the corresponding total information is also higher than the total_information_threshold, that feature will be considered admissible. The net information is the y-axis of the Core Infogram. Default is -1 which gets set to 0.1. | In |
relevance_index_threshold
double | A number between 0 and 1 representing a threshold for the relevance index, defaulting to 0.1. This is only used when protected_columns is set by the user. For a specific feature, if the relevance index value is higher than this threshold, and the corresponding safety index is also higher than the safety_index_threshold``, that feature will be considered admissible. The relevance index is the x-axis of the Fair Infogram. Default is -1 which gets set to 0.1. | In |
safety_index_threshold
double | A number between 0 and 1 representing a threshold for the safety index, defaulting to 0.1. This is only used when protected_columns is set by the user. For a specific feature, if the safety index value is higher than this threshold, and the corresponding relevance index is also higher than the relevance_index_threshold, that feature will be considered admissible. The safety index is the y-axis of the Fair Infogram. Default is -1 which gets set to 0.1. | In |
data_fraction
double | The fraction of training frame to use to build the infogram model. Defaults to 1.0, and any value greater than 0 and less than or equal to 1.0 is acceptable. | In |
top_n_features
int | An integer specifying the number of columns to evaluate in the infogram. The columns are ranked by variable importance, and the top N are evaluated. Defaults to 50. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
algorithm
enum | Type of machine learning algorithm used to build the infogram. Options include ‘AUTO’ (gbm), ‘deeplearning’ (Deep Learning with default parameters), ‘drf’ (Random Forest with default parameters), ‘gbm’ (GBM with default parameters), ‘glm’ (GLM with default parameters), or ‘xgboost’ (if available, XGBoost with default parameters). | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
InfogramV3
parameters
InfogramParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
InitIDV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
session_key
string | Session ID | In/Out |
session_properties_allowed
boolean | Indicates whether users are allowed to set and modify session properties | Out |
InputSchemaV4
_fields
string | Filter on the set of output fields: if you set _fields=”foo,bar,baz”, then only those fields will be included in the output; or you can specify _fields=”-goo,gee” to include all fields except goo and gee. If the result contains nested data structures, then you can refer to the fields within those structures as well. For example if you specify _fields=”foo(oof),bar(-rab)”, then only fields foo and bar will be included, and within foo there will be only field oof, whereas within bar all fields except rab will be reported. | In |
InteractionV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
dest
Key | destination key | In/Out |
source_frame
Key | Input data frame | In/Out |
factor_columns
string[] | Factor columns | In/Out |
pairwise
boolean | Whether to create pairwise quadratic interactions between factors (otherwise create one higher-order interaction). Only applicable if there are 3 or more factors. | In/Out |
max_factors
int | Max. number of factor levels in pair-wise interaction terms (if enforced, one extra catch-all factor will be made) | In/Out |
min_occurrence
int | Min. occurrence threshold for factor levels in pair-wise interaction terms | In/Out |
IoStatsEntry
backend
string | Back end type | Out |
store_count
long | Number of store events | Out |
store_bytes
long | Cumulative stored bytes | Out |
delete_count
long | Number of delete events | Out |
load_count
long | Number of load events | Out |
load_bytes
long | Cumulative loaded bytes | Out |
IsolationForestModelOutputV3
variable_splits
TwoDimTable | Variable Splits | Out |
variable_importances
TwoDimTable | Variable Importances | Out |
init_f
double | The Intercept term, the initial model function value to which trees make adjustments | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
IsolationForestModelV3
model_id
Key | Model key | In/Out |
parameters
IsolationForestParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
IsolationForestOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
IsolationForestParametersV3
sample_size
long | Number of randomly sampled observations used to train each Isolation Forest tree. Only one of parameters sample_size and sample_rate should be defined. If sample_rate is defined, sample_size will be ignored. | In |
sample_rate
double | Rate of randomly sampled observations used to train each Isolation Forest tree. Needs to be in range from 0.0 to 1.0. If set to -1, sample_rate is disabled and sample_size will be used instead. | In |
mtries
int | Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3. | In |
contamination
double | Contamination ratio - the proportion of anomalies in the input dataset. If undefined (-1) the predict function will not mark observations as anomalies and only anomaly score will be returned. Defaults to -1 (undefined). | In |
ntrees
int | Number of trees. | In |
max_depth
int | Maximum tree depth (0 for unlimited). | In |
min_rows
double | Fewest allowed (weighted) observations in a leaf. | In |
nbins
int | For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point | In |
nbins_top_level
int | For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level | In |
nbins_cats
int | For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. | In |
r2_stopping
double | r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
build_tree_one_node
boolean | Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. | In |
sample_rate_per_class
double[] | A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree | In |
col_sample_rate_per_tree
double | Column sample rate per tree (from 0.0 to 1.0) | In |
col_sample_rate_change_per_level
double | Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) | In |
score_tree_interval
int | Score the model after every so many trees. Disabled if set to 0. | In |
min_split_improvement
double | Minimum relative improvement in squared error reduction for a split to happen | In |
histogram_type
enum | What type of histogram to use for finding optimal split points | In |
calibrate_model
boolean | Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. | In |
in_training_checkpoints_dir
string | Create checkpoints into defined directory while training process is still running. In case of cluster shutdown, this checkpoint can be used to restart training. | In |
in_training_checkpoints_tree_interval
int | Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is defined | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
validation_response_column
VecSpecifier | (experimental) Name of the response column in the validation frame. Response column should be binary and indicate not anomaly/anomaly. | In/Out |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
calibration_frame
Key | Data for model calibration | In/Out |
calibration_method
enum | Calibration method to use | In/Out |
check_constant_response
boolean | Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
IsolationForestV3
parameters
IsolationForestParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
IsotonicRegressionModelOutputV3
thresholds_y
double[] | thresholds y | In |
thresholds_x
double[] | thresholds X | In |
min_x
double | min X | In |
max_x
double | max X | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
IsotonicRegressionModelV3
model_id
Key | Model key | In/Out |
parameters
IsotonicRegressionParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
IsotonicRegressionOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
IsotonicRegressionParametersV3
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
out_of_bounds
enum | Method of handling values of X predictor that are outside of the bounds seen in training. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
IsotonicRegressionV3
parameters
IsotonicRegressionParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
JStackV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
traces
DStackTrace[] | Stacktraces | Out |
JobIV4
job_id
string | Id of the job to fetch. | In |
_fields
string | Filter on the set of output fields: if you set _fields=”foo,bar,baz”, then only those fields will be included in the output; or you can specify _fields=”-goo,gee” to include all fields except goo and gee. If the result contains nested data structures, then you can refer to the fields within those structures as well. For example if you specify _fields=”foo(oof),bar(-rab)”, then only fields foo and bar will be included, and within foo there will be only field oof, whereas within bar all fields except rab will be reported. | In |
JobKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
JobV3
key
Key | Job Key | In |
description
string | Job description | In |
dest
Key | destination key | In/Out |
status
string | job status | Out |
progress
float | progress, from 0 to 1 | Out |
progress_msg
string | current progress status description | Out |
start_time
long | Start time | Out |
msec
long | Runtime in milliseconds | Out |
warnings
string[] | exception | Out |
exception
string | exception | Out |
stacktrace
string | stacktrace | Out |
auto_recoverable
boolean | recoverable | Out |
ready_for_view
boolean | ready for view | Out |
JobV4
job_id
string | Job id | In |
status
enum | Job status | In |
progress
float | Current progress, a number going from 0 to 1 | In |
progress_msg
string | Current progress status description | In |
start_time
long | Start time | In |
duration
long | Runtime in milliseconds | In |
target_id
string | Id of the target object (being created by this Job) | In |
target_type
string | Type of the target: Frame, Model, etc. | In |
exception
string | Exception message, if an exception occurred | In |
stacktrace
string | Stacktrace | In |
__schema
string | Url describing the schema of the current object. | In |
JobsV3
job_id
Key | Optional Job identifier | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
jobs
Job[] | jobs | Out |
KMeansModelOutputV3
centers
TwoDimTable | Cluster Centers[k][features] | In |
centers_std
TwoDimTable | Cluster Centers[k][features] on Standardized Data | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
KMeansModelV3
model_id
Key | Model key | In/Out |
parameters
KMeansParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
KMeansOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
KMeansParametersV3
user_points
Key | This option allows you to specify a dataframe, where each row represents an initial cluster center. The user-specified points must have the same number of columns as the training observations. The number of rows must equal the number of clusters | In |
max_iterations
int | Maximum training iterations (if estimate_k is enabled, then this is for each inner Lloyds iteration) | In |
standardize
boolean | Standardize columns before computing distances | In |
seed
long | RNG Seed | In |
init
enum | Initialization mode | In |
estimate_k
boolean | Whether to estimate the number of clusters (<=k) iteratively and deterministically. | In |
cluster_size_constraints
int[] | An array specifying the minimum number of points that should be in each cluster. The length of the constraints array has to be the same as the number of clusters. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
k
int | The max. number of clusters. If estimate_k is disabled, the model will find k centroids, otherwise it will find up to k centroids. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
KMeansV3
parameters
KMeansParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
KeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
KeyValueV3
key
string | Key | In |
value
double | Value | In |
KillMinus3V3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
LeaderboardV99
project_name
string | Identifier for models that should be grouped together in the leaderboard | In/Out |
sort_metric
string | Metric used to sort this leaderboard | In/Out |
sort_decreasing
boolean | Metric direction used in the sort | In/Out |
models
Key[] | List of models for this leaderboard, sorted by metric so that the best is first | Out |
leaderboard_frame
Key | Frame for this leaderboard | Out |
leaderboard_frame_checksum
long | Checksum for the Frame for this leaderboard | Out |
sort_metrics
double[] | Sort metrics for the models in this leaderboard, in the same order as the models | Out |
table
TwoDimTable | A table representation of this leaderboard, for easy rendering | Out |
LeaderboardsV99
project_name
string | Name of project of interest | In |
extensions
string[] | List of extension columns to add to leaderboard | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
leaderboards
Leaderboard[] | Leaderboards | Out |
ListRequestV4
__schema
string | Url describing the schema of the current object. | In |
LogAndEchoV3
message
string | Message to be Logged and Echoed | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
LogsV3
nodeidx
string | Identifier of the node to get logs from. It can be either node index starting from (0-based), where -1 means current node, or IP and port. | In |
name
string | Which specific log file to read from the log file directory. If left unspecified, the system chooses a default for you. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
log
string | Content of log file | Out |
MakeGLMModelV3
model
Key | source model | In |
dest
Key | destination key | In |
names
string[] | coefficient names | In |
beta
double[] | new glm coefficients | In |
threshold
float | decision threshold for label-generation | In |
MetadataV3
num
int | Number for specifying an endpoint | In |
http_method
string | HTTP method (GET, POST, DELETE) if fetching by path | In |
path
string | Path for specifying an endpoint | In |
classname
string | Class name, for fetching docs for a schema (DEPRECATED) | In |
schemaname
string | Schema name (e.g., DocsV1), for fetching docs for a schema | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
routes
Route[] | List of endpoint routes | Out |
schemas
SchemaMetadata[] | List of schemas | Out |
markdown
string | Table of Contents Markdown | Out |
MissingInserterV3
dataset
Key | dataset | In |
fraction
double | Fraction of data to replace with a missing value | In |
seed
long | Seed | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
ModelBuilderSchema
parameters
Parameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
ModelBuilderV3
parameters
Parameters | Model builder parameters. | Out |
messages
ValidationMessage[] | Info, warning and error messages; NOTE: can be appended to while the Job is running | Out |
error_count
int | Count of error messages | Out |
ModelBuildersV3
algo
string | Algo of ModelBuilder of interest | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
model_builders
Map | ModelBuilders | Out |
ModelExportV3
model_id
Key | Name of Model of interest | In |
dir
string | Destination file (hdfs, s3, local) | In |
force
boolean | Overwrite destination file in case it exists or throw exception if set to false. | In |
export_cross_validation_predictions
boolean | Flag indicating whether the exported model artifact should also include CV Holdout Frame predictions | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
ModelIdV3
model_id
string | Model ID | Out |
ModelImportV3
model_id
Key | Save imported model under given key into DKV. | In |
dir
string | Source directory (hdfs, s3, local) containing serialized model | In |
force
boolean | Override existing model in case it exists or throw exception if set to false | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
ModelInfoV4
algo
string | Algorithm name, such as ‘gbm’, ‘deeplearning’, etc. | In |
maturity
string | Development status of the algorithm: alpha, beta, or stable. | In |
have_pojo
boolean | Does the model support generation of POJOs? | In |
have_mojo
boolean | Does the model support generation of MOJOs? | In |
mojo_version
string | Mojo version number for this algorithm. | In |
__schema
string | Url describing the schema of the current object. | In |
ModelKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
ModelMetricsAnomalyV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
mean_score
double | Mean Anomaly Score. | Out |
mean_normalized_score
double | Mean Normalized Anomaly Score. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsAutoEncoderV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsBaseV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsBinomialGLMGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
coefficients_table
TwoDimTable | coefficients_table | Out |
r2
double | The R^2 for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
AUC
double | The AUC for this scoring run. | Out |
pr_auc
double | The precision-recall AUC for this scoring run. | Out |
Gini
double | The Gini score for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
domain
string[] | The class labels of the response. | Out |
cm
ConfusionMatrix | The ConfusionMatrix at the threshold for maximum F1. | Out |
thresholds_and_metric_scores
TwoDimTable | The Metrics for various thresholds. | Out |
max_criteria_and_metric_scores
TwoDimTable | The Metrics for various criteria. | Out |
gains_lift_table
TwoDimTable | Gains and Lift table. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsBinomialGLMV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
r2
double | The R^2 for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
AUC
double | The AUC for this scoring run. | Out |
pr_auc
double | The precision-recall AUC for this scoring run. | Out |
Gini
double | The Gini score for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
domain
string[] | The class labels of the response. | Out |
cm
ConfusionMatrix | The ConfusionMatrix at the threshold for maximum F1. | Out |
thresholds_and_metric_scores
TwoDimTable | The Metrics for various thresholds. | Out |
max_criteria_and_metric_scores
TwoDimTable | The Metrics for various criteria. | Out |
gains_lift_table
TwoDimTable | Gains and Lift table. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsBinomialGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
r2
double | The R^2 for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
AUC
double | The AUC for this scoring run. | Out |
pr_auc
double | The precision-recall AUC for this scoring run. | Out |
Gini
double | The Gini score for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
domain
string[] | The class labels of the response. | Out |
cm
ConfusionMatrix | The ConfusionMatrix at the threshold for maximum F1. | Out |
thresholds_and_metric_scores
TwoDimTable | The Metrics for various thresholds. | Out |
max_criteria_and_metric_scores
TwoDimTable | The Metrics for various criteria. | Out |
gains_lift_table
TwoDimTable | Gains and Lift table. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsBinomialUpliftGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
ate
double | Average Treatment Effect. | Out |
att
double | Average Treatment Effect on the Treated. | Out |
atc
double | Average Treatment Effect on the Control. | Out |
AUUC
double | The default AUUC for this scoring run. | Out |
auuc_normalized
double | The default normalized AUUC for this scoring run. | Out |
qini
double | The Qini value for this scoring run. | Out |
domain
string[] | The class labels of the response. | Out |
thresholds_and_metric_scores
TwoDimTable | The metrics for various thresholds. | Out |
auuc_table
TwoDimTable | Table of all types of AUUC. | Out |
aecu_table
TwoDimTable | Table of all types of AECU values. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsBinomialUpliftV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
ate
double | Average Treatment Effect. | Out |
att
double | Average Treatment Effect on the Treated. | Out |
atc
double | Average Treatment Effect on the Control. | Out |
AUUC
double | The default AUUC for this scoring run. | Out |
auuc_normalized
double | The default normalized AUUC for this scoring run. | Out |
qini
double | The Qini value for this scoring run. | Out |
domain
string[] | The class labels of the response. | Out |
thresholds_and_metric_scores
TwoDimTable | The metrics for various thresholds. | Out |
auuc_table
TwoDimTable | Table of all types of AUUC. | Out |
aecu_table
TwoDimTable | Table of all types of AECU values. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsBinomialV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
r2
double | The R^2 for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
AUC
double | The AUC for this scoring run. | Out |
pr_auc
double | The precision-recall AUC for this scoring run. | Out |
Gini
double | The Gini score for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
domain
string[] | The class labels of the response. | Out |
cm
ConfusionMatrix | The ConfusionMatrix at the threshold for maximum F1. | Out |
thresholds_and_metric_scores
TwoDimTable | The Metrics for various thresholds. | Out |
max_criteria_and_metric_scores
TwoDimTable | The Metrics for various criteria. | Out |
gains_lift_table
TwoDimTable | Gains and Lift table. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsClusteringV3
tot_withinss
double | Within Cluster Sum of Square Error | In |
totss
double | Total Sum of Square Error to Grand Mean | In |
betweenss
double | Between Cluster Sum of Square Error | In |
centroid_stats
TwoDimTable | Centroid Statistics | In |
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsGLRMV99
numerr
double | Sum of Squared Error (Numeric Cols) | In |
caterr
double | Misclassification Error (Categorical Cols) | In |
numcnt
long | Number of Non-Missing Numeric Values | In |
catcnt
long | Number of Non-Missing Categorical Values | In |
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsHGLMGaussianGaussianGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
sefe
double[] | standard error of fixed predictors/effects | Out |
sere
double[] | standard error of random effects | Out |
varfix
double | dispersion parameter of the mean model (residual variance for LMM) | Out |
varranef
double[] | dispersion parameter of the random effects (variance of random effects for GLMM | Out |
fixef
double[] | fixed coefficient) | Out |
ranef
double[] | random coefficients | Out |
converge
boolean | true if model has converged | Out |
randc
int[] | number of random columns | Out |
dfrefe
double | deviance degrees of freedom for mean part of the model | Out |
summvc1
double[] | estimates, standard errors of the linear predictor in the dispersion model | Out |
summvc2
double[][] | estimates, standard errors of the linear predictor for dispersion parameter of random effects | Out |
hlik
double | log h-likelihood | Out |
pvh
double | adjusted profile log-likelihood profiled over random effects | Out |
pbvh
double | adjusted profile log-likelihood profiled over fixed and random effects | Out |
caic
double | conditional AIC | Out |
bad
long | index of the most influential observation | Out |
sumetadiffsquare
double | sum(etai-eta0)^2 where etai is current eta and eta0 is the previous one | Out |
convergence
double | sum(etai-eta0)^2/sum(etai)^2 | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsHGLMGaussianGaussianV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
sefe
double[] | standard error of fixed predictors/effects | Out |
sere
double[] | standard error of random effects | Out |
varfix
double | dispersion parameter of the mean model (residual variance for LMM) | Out |
varranef
double[] | dispersion parameter of the random effects (variance of random effects for GLMM | Out |
fixef
double[] | fixed coefficient) | Out |
ranef
double[] | random coefficients | Out |
converge
boolean | true if model has converged | Out |
randc
int[] | number of random columns | Out |
dfrefe
double | deviance degrees of freedom for mean part of the model | Out |
summvc1
double[] | estimates, standard errors of the linear predictor in the dispersion model | Out |
summvc2
double[][] | estimates, standard errors of the linear predictor for dispersion parameter of random effects | Out |
hlik
double | log h-likelihood | Out |
pvh
double | adjusted profile log-likelihood profiled over random effects | Out |
pbvh
double | adjusted profile log-likelihood profiled over fixed and random effects | Out |
caic
double | conditional AIC | Out |
bad
long | index of the most influential observation | Out |
sumetadiffsquare
double | sum(etai-eta0)^2 where etai is current eta and eta0 is the previous one | Out |
convergence
double | sum(etai-eta0)^2/sum(etai)^2 | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsHGLMGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
sefe
double[] | standard error of fixed predictors/effects | Out |
sere
double[] | standard error of random effects | Out |
varfix
double | dispersion parameter of the mean model (residual variance for LMM) | Out |
varranef
double[] | dispersion parameter of the random effects (variance of random effects for GLMM | Out |
fixef
double[] | fixed coefficient) | Out |
ranef
double[] | random coefficients | Out |
converge
boolean | true if model has converged | Out |
randc
int[] | number of random columns | Out |
dfrefe
double | deviance degrees of freedom for mean part of the model | Out |
summvc1
double[] | estimates, standard errors of the linear predictor in the dispersion model | Out |
summvc2
double[][] | estimates, standard errors of the linear predictor for dispersion parameter of random effects | Out |
hlik
double | log h-likelihood | Out |
pvh
double | adjusted profile log-likelihood profiled over random effects | Out |
pbvh
double | adjusted profile log-likelihood profiled over fixed and random effects | Out |
caic
double | conditional AIC | Out |
bad
long | index of the most influential observation | Out |
sumetadiffsquare
double | sum(etai-eta0)^2 where etai is current eta and eta0 is the previous one | Out |
convergence
double | sum(etai-eta0)^2/sum(etai)^2 | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsHGLMV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
sefe
double[] | standard error of fixed predictors/effects | Out |
sere
double[] | standard error of random effects | Out |
varfix
double | dispersion parameter of the mean model (residual variance for LMM) | Out |
varranef
double[] | dispersion parameter of the random effects (variance of random effects for GLMM | Out |
fixef
double[] | fixed coefficient) | Out |
ranef
double[] | random coefficients | Out |
converge
boolean | true if model has converged | Out |
randc
int[] | number of random columns | Out |
dfrefe
double | deviance degrees of freedom for mean part of the model | Out |
summvc1
double[] | estimates, standard errors of the linear predictor in the dispersion model | Out |
summvc2
double[][] | estimates, standard errors of the linear predictor for dispersion parameter of random effects | Out |
hlik
double | log h-likelihood | Out |
pvh
double | adjusted profile log-likelihood profiled over random effects | Out |
pbvh
double | adjusted profile log-likelihood profiled over fixed and random effects | Out |
caic
double | conditional AIC | Out |
bad
long | index of the most influential observation | Out |
sumetadiffsquare
double | sum(etai-eta0)^2 where etai is current eta and eta0 is the previous one | Out |
convergence
double | sum(etai-eta0)^2/sum(etai)^2 | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsListSchemaV3
model
Key | Key of Model of interest (optional) | In |
frame
Key | Key of Frame of interest (optional) | In |
reconstruction_error
boolean | Compute reconstruction error (optional, only for Deep Learning AutoEncoder models) | In |
reconstruction_error_per_feature
boolean | Compute reconstruction error per feature (optional, only for Deep Learning AutoEncoder models) | In |
deep_features_hidden_layer
int | Extract Deep Features for given hidden layer (optional, only for Deep Learning models) | In |
deep_features_hidden_layer_name
string | Extract Deep Features for given hidden layer by name (optional, only for Deep Water models) | In |
reconstruct_train
boolean | Reconstruct original training frame (optional, only for GLRM models) | In |
project_archetypes
boolean | Project GLRM archetypes back into original feature space (optional, only for GLRM models) | In |
reverse_transform
boolean | Reverse transformation applied during training to model output (optional, only for GLRM models) | In |
leaf_node_assignment
boolean | Return the leaf node assignment (optional, only for DRF/GBM models) | In |
leaf_node_assignment_type
enum | Type of the leaf node assignment (optional, only for DRF/GBM models) | In |
predict_staged_proba
boolean | Predict the class probabilities at each stage (optional, only for GBM models) | In |
predict_contributions
boolean | Predict the feature contributions - Shapley values (optional, only for DRF, GBM and XGBoost models) | In |
row_to_tree_assignment
boolean | Return which row is used in which tree (optional, only for GBM models) | In |
predict_contributions_output_format
enum | Specify how to output feature contributions in XGBoost - XGBoost by default outputs contributions for 1-hot encoded features, specifying a Compact output format will produce a per-feature contribution | In |
top_n
int | Only for predict_contributions function - sort Shapley values and return top_n highest (optional) | In |
bottom_n
int | Only for predict_contributions function - sort Shapley values and return bottom_n lowest (optional) | In |
compare_abs
boolean | Only for predict_contributions function - sort absolute Shapley values (optional) | In |
feature_frequencies
boolean | Retrieve the feature frequencies on paths in trees in tree-based models (optional, only for GBM, DRF and Isolation Forest) | In |
exemplar_index
int | Retrieve all members for a given exemplar (optional, only for Aggregator models) | In |
deviances
boolean | Compute the deviances per row (optional, only for classification or regression models) | In |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In |
auc_type
string | Set default multinomial AUC type. Must be one of: “AUTO”, “NONE”, “MACRO_OVR”, “WEIGHTED_OVR”, “MACRO_OVO”, “WEIGHTED_OVO”. Default is “NONE” (optional, only for multinomial classification). | In |
auuc_type
string | Set default AUUC type for uplift binomial classification. Must be one of: “AUTO”, “qini”, “lift”, “gain”. Default is “AUTO” (optional, only for uplift binomial classification). | In |
auuc_nbins
int | Set number of bins to calculate AUUC. Must be -1 or higher than 0. Default is -1 which means 1000 (optional, only for uplift binomial classification). | In |
background_frame
Key | Specify background frame used as a reference for calculating SHAP. | In |
output_space
boolean | If true, transform contributions so that they sum up to the difference in the output space (applicable iff contributions are in link space). Note that this transformation is an approximation and the contributions won’t be exact SHAP values. | In |
output_per_reference
boolean | If true, return contributions against each background sample (aka reference), i.e. phi(feature, x, bg), otherwise return contributions averaged over the background sample (phi(feature, x) = E_{bg} phi(feature, x, bg)) | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
predictions_frame
Key | Key of predictions frame, if predictions are requested (optional) | In/Out |
deviances_frame
Key | Key for the frame containing per-observation deviances (optional) | In/Out |
custom_auuc_thresholds
double[] | Custom AUUC thresholds (for uplift binomial classification). | In/Out |
model_metrics
ModelMetrics[] | ModelMetrics | Out |
ModelMetricsMakerSchemaV3
predictions_frame
string | Predictions Frame. | In/Out |
actuals_frame
string | Actuals Frame. | In/Out |
weights_frame
string | Weights Frame. | In/Out |
treatment_frame
string | Treatment Frame. | In/Out |
domain
string[] | Domain (for classification). | In/Out |
distribution
enum | Distribution (for regression). | In/Out |
auc_type
enum | Default AUC type (for multinomial classification). | In/Out |
auuc_type
enum | Default AUUC type (for uplift binomial classification). | In/Out |
auuc_nbins
int | Number of bins to calculate AUUC (for uplift binomial classification). | In/Out |
custom_auuc_thresholds
double[] | Custom AUUC thresholds (for uplift binomial classification). | In/Out |
model_metrics
ModelMetrics | Model Metrics. | Out |
ModelMetricsMultinomialGLMGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
coefficients_table
TwoDimTable | coefficients_table | Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
AUC
double | The average AUC for this scoring run. | Out |
pr_auc
double | The average precision-recall AUC for this scoring run. | Out |
multinomial_auc_table
TwoDimTable | The multinomial AUC values. | Out |
multinomial_aucpr_table
TwoDimTable | The multinomial PR AUC values. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsMultinomialGLMV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
AUC
double | The average AUC for this scoring run. | Out |
pr_auc
double | The average precision-recall AUC for this scoring run. | Out |
multinomial_auc_table
TwoDimTable | The multinomial AUC values. | Out |
multinomial_aucpr_table
TwoDimTable | The multinomial PR AUC values. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsMultinomialGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
AUC
double | The average AUC for this scoring run. | Out |
pr_auc
double | The average precision-recall AUC for this scoring run. | Out |
multinomial_auc_table
TwoDimTable | The multinomial AUC values. | Out |
multinomial_aucpr_table
TwoDimTable | The multinomial PR AUC values. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsMultinomialV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
AUC
double | The average AUC for this scoring run. | Out |
pr_auc
double | The average precision-recall AUC for this scoring run. | Out |
multinomial_auc_table
TwoDimTable | The multinomial AUC values. | Out |
multinomial_aucpr_table
TwoDimTable | The multinomial PR AUC values. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsOrdinalGLMGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
coefficients_table
TwoDimTable | coefficients_table | Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsOrdinalGLMV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsOrdinalGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsOrdinalV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
r2
double | The R^2 for this scoring run. | Out |
hit_ratio_table
TwoDimTable | The hit ratio table for this scoring run. | Out |
cm
ConfusionMatrix | The ConfusionMatrix object for this scoring run. | Out |
logloss
double | The logarithmic loss for this scoring run. | Out |
mean_per_class_error
double | The mean misclassification error per class. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsPCAV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsRegressionCoxPHGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
concordance
double | Concordance metric (c-index) | Out |
concordant
long | Number of concordant pairs | Out |
discordant
long | Number of discordant pairs. | Out |
tied_y
long | Number of tied pairs | Out |
r2
double | The R^2 for this scoring run. | Out |
mean_residual_deviance
double | The mean residual deviance for this scoring run. | Out |
mae
double | The mean absolute error for this scoring run. | Out |
rmsle
double | The root mean squared log error for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsRegressionCoxPHV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
concordance
double | concordance index | Out |
concordant
long | number of concordant pairs | Out |
discordant
long | number of discordant pairs | Out |
tied_y
long | number of pairs tied in Y value | Out |
r2
double | The R^2 for this scoring run. | Out |
mean_residual_deviance
double | The mean residual deviance for this scoring run. | Out |
mae
double | The mean absolute error for this scoring run. | Out |
rmsle
double | The root mean squared log error for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsRegressionGLMGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
coefficients_table
TwoDimTable | coefficients_table | Out |
r2
double | The R^2 for this scoring run. | Out |
mean_residual_deviance
double | The mean residual deviance for this scoring run. | Out |
mae
double | The mean absolute error for this scoring run. | Out |
rmsle
double | The root mean squared log error for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsRegressionGLMV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
residual_deviance
double | residual deviance | Out |
null_deviance
double | null deviance | Out |
AIC
double | AIC | Out |
loglikelihood
double | log likelihood | Out |
null_degrees_of_freedom
long | null DOF | Out |
residual_degrees_of_freedom
long | residual DOF | Out |
r2
double | The R^2 for this scoring run. | Out |
mean_residual_deviance
double | The mean residual deviance for this scoring run. | Out |
mae
double | The mean absolute error for this scoring run. | Out |
rmsle
double | The root mean squared log error for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsRegressionGenericV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
mean_residual_deviance
double | The mean residual deviance for this scoring run. | Out |
mae
double | The mean absolute error for this scoring run. | Out |
rmsle
double | The root mean squared log error for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsRegressionV3
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
r2
double | The R^2 for this scoring run. | Out |
mean_residual_deviance
double | The mean residual deviance for this scoring run. | Out |
mae
double | The mean absolute error for this scoring run. | Out |
rmsle
double | The root mean squared log error for this scoring run. | Out |
loglikelihood
double | The logarithmic likelihood for this scoring run. | Out |
AIC
double | The AIC for this scoring run. | Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelMetricsSVDV99
nobs
long | Number of observations. | In |
model
Key | The model used for this scoring run. | In/Out |
model_checksum
long | The checksum for the model used for this scoring run. | In/Out |
frame
Key | The frame used for this scoring run. | In/Out |
frame_checksum
long | The checksum for the frame used for this scoring run. | In/Out |
description
string | Optional description for this scoring run (to note out-of-bag, sampled data, etc.) | Out |
model_category
enum | The category (e.g., Clustering) for the model used for this scoring run. | Out |
scoring_time
long | The time in mS since the epoch for the start of this scoring run. | Out |
predictions
Frame | Predictions Frame. | Out |
MSE
double | The Mean Squared Error of the prediction for this scoring run. | Out |
RMSE
double | The Root Mean Squared Error of the prediction for this scoring run. | Out |
custom_metric_name
string | Name of custom metric | Out |
custom_metric_value
double | Value of custom metric | Out |
ModelOutputSchemaV3
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
ModelParameterSchemaV3
is_member_of_frames
string[] | For Vec-type fields this is the set of Frame-type fields which must contain the named column; for example, for a SupervisedModel the response_column must be in both the training_frame and (if it’s set) the validation_frame | In |
is_mutually_exclusive_with
string[] | For Vec-type fields this is the set of other Vec-type fields which must contain mutually exclusive values; for example, for a SupervisedModel the response_column must be mutually exclusive with the weights_column | In |
name
string | name in the JSON, e.g. “lambda” | Out |
label
string | [DEPRECATED] same as name. | Out |
help
string | help for the UI, e.g. “regularization multiplier, typically used for foo bar baz etc.” | Out |
required
boolean | the field is required | Out |
type
string | Java type, e.g. “double” | Out |
default_value
Polymorphic | default value, e.g. 1 | Out |
actual_value
Polymorphic | actual value as set by the user and / or modified by the ModelBuilder, e.g., 10 | Out |
input_value
Polymorphic | input value as set by the user, e.g., 10 | Out |
level
string | the importance of the parameter, used by the UI, e.g. “critical”, “extended” or “expert” | Out |
values
string[] | list of valid values for use by the front-end | Out |
gridable
boolean | Parameter can be used in grid call | Out |
ModelParametersSchemaV3
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
ModelSchemaBaseV3
model_id
Key | Model key | In/Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
ModelSchemaV3
model_id
Key | Model key | In/Out |
parameters
Parameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
Output | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
ModelSelectionModelOutputV3
best_predictors_subset
string[][] | Names of predictors in the best predictor subset | In |
best_r2_values
double[] | R2 values of all possible predictor subsets. Only for mode=’allsubsets’ or ‘maxr’. | In |
predictors_added_per_step
string[][] | at each predictor subset size, the predictor added is collected in this array. Not for mode = ‘backward’. | In |
predictors_removed_per_step
string[][] | at each predictor subset size, the predictor removed is collected in this array. | In |
coef_p_values
double[][] | p-values of chosen predictor subsets at each subset size. Only for model=’backward’. | In |
z_values
double[][] | z-values of chosen predictor subsets at each subset size. Only for model=’backward’. | In |
best_model_ids
Key[] | Key of models containing best 1-predictor model, best 2-predictors model, …. | In |
coefficient_names
string[][] | arrays of string arrays containing coefficient names of best 1-predictor model, best 2-predictors model, …. | In |
coefficient_values
double[][] | store coefficient values for each predictor subset. Only for maxrsweep when build_glm_model is false. | In |
coefficient_values_normalized
double[][] | store standardized coefficient values for each predictor subset. Only for maxrsweep when build_glm_model is false. | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
ModelSelectionModelV3
model_id
Key | Model key | In/Out |
parameters
ModelSelectionParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
ModelSelectionModelOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
ModelSelectionParametersV3
seed
long | Seed for pseudo random number generator (if applicable) | In |
family
enum | Family. For maxr/maxrsweep, only gaussian. For backward, ordinal and multinomial families are not supported | In |
tweedie_variance_power
double | Tweedie variance power | In |
tweedie_link_power
double | Tweedie link power | In |
theta
double | Theta | In |
solver
enum | AUTO will set the solver based on given data and the other parameters. IRLSM is fast on on problems with small number of predictors and for lambda-search with L1 penalty, L_BFGS scales better for datasets with many columns. | In |
alpha
double[] | Distribution of regularization between the L1 (Lasso) and L2 (Ridge) penalties. A value of 1 for alpha represents Lasso regression, a value of 0 produces Ridge regression, and anything in between specifies the amount of mixing between the two. Default value of alpha is 0 when SOLVER = ‘L-BFGS’; 0.5 otherwise. | In |
lambda
double[] | Regularization strength | In |
lambda_search
boolean | Use lambda search starting at lambda max, given lambda is then interpreted as lambda min | In |
multinode_mode
boolean | For maxrsweep only. If enabled, will attempt to perform sweeping action using multiple nodes in the cluster. Defaults to false. | In |
build_glm_model
boolean | For maxrsweep mode only. If true, will return full blown GLM models with the desired predictorsubsets. If false, only the predictor subsets, predictor coefficients are returned. This is forspeeding up the model selection process. The users can choose to build the GLM models themselvesby using the predictor subsets themselves. Defaults to false. | In |
early_stopping
boolean | Stop early when there is no more relative improvement on train or validation (if provided) | In |
nlambdas
int | Number of lambdas to be used in a search. Default indicates: If alpha is zero, with lambda search set to True, the value of nlamdas is set to 30 (fewer lambdas are needed for ridge regression) otherwise it is set to 100. | In |
score_iteration_interval
int | Perform scoring for every score_iteration_interval iterations | In |
standardize
boolean | Standardize numeric columns to have zero mean and unit variance | In |
cold_start
boolean | Only applicable to multiple alpha/lambda values. If false, build the next model for next set of alpha/lambda values starting from the values provided by current model. If true will start GLM model from scratch. | In |
plug_values
Key | Plug Values (a single row frame containing values that will be used to impute missing values of the training/validation frame, use with conjunction missing_values_handling = PlugValues) | In |
non_negative
boolean | Restrict coefficients (not intercept) to be non-negative | In |
max_iterations
int | Maximum number of iterations | In |
beta_epsilon
double | Converge if beta changes less (using L-infinity norm) than beta esilon, ONLY applies to IRLSM solver | In |
objective_epsilon
double | Converge if objective value changes less than this. Default (of -1.0) indicates: If lambda_search is set to True the value of objective_epsilon is set to .0001. If the lambda_search is set to False and lambda is equal to zero, the value of objective_epsilon is set to .000001, for any other value of lambda the default value of objective_epsilon is set to .0001. | In |
gradient_epsilon
double | Converge if objective changes less (using L-infinity norm) than this, ONLY applies to L-BFGS solver. Default (of -1.0) indicates: If lambda_search is set to False and lambda is equal to zero, the default value of gradient_epsilon is equal to .000001, otherwise the default value is .0001. If lambda_search is set to True, the conditional values above are 1E-8 and 1E-6 respectively. | In |
obj_reg
double | Likelihood divider in objective value computation, default (of -1.0) will set it to 1/nobs | In |
link
enum | Link function. | In |
startval
double[] | double array to initialize fixed and random coefficients for HGLM, coefficients for GLM. | In |
calc_like
boolean | if true, will return likelihood function value for HGLM. | In |
intercept
boolean | Include constant term in the model | In |
prior
double | Prior probability for y==1. To be used only for logistic regression iff the data has been sampled and the mean of response does not reflect reality. | In |
lambda_min_ratio
double | Minimum lambda used in lambda search, specified as a ratio of lambda_max (the smallest lambda that drives all coefficients to zero). Default indicates: if the number of observations is greater than the number of variables, then lambda_min_ratio is set to 0.0001; if the number of observations is less than the number of variables, then lambda_min_ratio is set to 0.01. | In |
beta_constraints
Key | Beta constraints | In |
max_active_predictors
int | Maximum number of active predictors during computation. Use as a stopping criterion to prevent expensive model building with many predictors. Default indicates: If the IRLSM solver is used, the value of max_active_predictors is set to 5000 otherwise it is set to 100000000. | In |
compute_p_values
boolean | Request p-values computation, p-values work only with IRLSM solver and no regularization | In |
remove_collinear_columns
boolean | In case of linearly dependent columns, remove some of the dependent columns | In |
max_predictor_number
int | Maximum number of predictors to be considered when building GLM models. Defaults to 1. | In |
min_predictor_number
int | For mode = ‘backward’ only. Minimum number of predictors to be considered when building GLM models starting with all predictors to be included. Defaults to 1. | In |
nparallelism
int | number of models to build in parallel. Defaults to 0.0 which is adaptive to the system capability | In |
p_values_threshold
double | For mode=’backward’ only. If specified, will stop the model building process when all coefficientsp-values drop below this threshold | In |
influence
enum | If set to dfbetas will calculate the difference in beta when a datarow is included and excluded in the dataset. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
missing_values_handling
enum | Handling of missing values. Either MeanImputation, Skip or PlugValues. | In/Out |
mode
enum | Mode: Used to choose model selection algorithms to use. Options include ‘allsubsets’ for all subsets, ‘maxr’ that uses sequential replacement and GLM to build all models, slow but works with cross-validation, validation frames for more robust results, ‘maxrsweep’ that uses sequential replacement and sweeping action, much faster than ‘maxr’, ‘backward’ for backward selection. | In/Out |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
ModelSelectionV3
parameters
ModelSelectionParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
ModelSynopsisV3
model_id
Key | Model key | In/Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
ModelsInfoV4
models
ModelBuilder[] | Generic information about each model supported in H2O. | In |
__schema
string | Url describing the schema of the current object. | In |
ModelsKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
ModelsV3
model_id
Key | Name of Model of interest | In |
preview
boolean | Return potentially abridged model suitable for viewing in a browser | In |
find_compatible_frames
boolean | Find and return compatible frames? | In |
export_cross_validation_predictions
boolean | Flag indicating whether the exported model artifact should also include CV Holdout Frame predictions | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
models
Model[] | Models | Out |
compatible_frames
Frame[] | Compatible frames | Out |
NaiveBayesModelOutputV3
levels
string[] | Categorical levels of the response | In |
apriori
TwoDimTable | A-priori probabilities of the response | In |
pcond
TwoDimTable[] | Conditional probabilities of the predictors | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
NaiveBayesModelV3
model_id
Key | Model key | In/Out |
parameters
NaiveBayesParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
NaiveBayesOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
NaiveBayesParametersV3
laplace
double | Laplace smoothing parameter | In |
min_sdev
double | Min. standard deviation to use for observations with not enough data | In |
eps_sdev
double | Cutoff below which standard deviation is replaced with min_sdev | In |
min_prob
double | Min. probability to use for observations with not enough data | In |
eps_prob
double | Cutoff below which probability is replaced with min_prob | In |
compute_metrics
boolean | Compute metrics on training data | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
seed
long | Seed for pseudo random number generator (only used for cross-validation and fold_assignment=”Random” or “AUTO”) | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
NaiveBayesV3
parameters
NaiveBayesParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
NetworkBenchV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
results
TwoDimTable[] | NetworkBenchResults | Out |
NetworkEvent
is_send
boolean | Boolean flag distinguishing between sends (true) and receives(false) | In |
protocol
string | network protocol (UDP/TCP) | In |
msg_type
string | UDP type (exec,ack, ackack,… | In |
from
string | Sending node | In |
to
string | Receiving node | In |
data
string | Pretty print of the first few bytes of the msg payload. Contains class name for tasks. | In |
date
string | Time when the event was recorded. Format is hh:mm:ss:ms | In |
nanos
long | Time in nanos | In |
type
enum | type of recorded event | In |
NetworkTestV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
microseconds_collective
double[] | Collective broadcast/reduce times in microseconds (for each message size) | Out |
bandwidths_collective
double[] | Collective bandwidths in Bytes/sec (for each message size, for each node) | Out |
microseconds
double[][] | Round-trip times in microseconds (for each message size, for each node) | Out |
bandwidths
double[][] | Bi-directional bandwidths in Bytes/sec (for each message size, for each node) | Out |
nodes
string[] | Nodes | Out |
table
TwoDimTable | NetworkTestResults | Out |
NodeMemoryInfoV3
ip_port
string | IP address and port in the form a.b.c.d:e | Out |
free_mem
long | Free heap | Out |
NodePersistentStorageEntryV3
category
string | Category name | Out |
name
string | Key name | Out |
size
long | Size in bytes of value | Out |
timestamp_millis
long | Epoch time in milliseconds of when the value was written | Out |
NodePersistentStorageV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
category
string | Category name | In/Out |
name
string | Key name | In/Out |
value
string | Value | In/Out |
configured
boolean | Configured | Out |
exists
boolean | Exists | Out |
entries
Iced[] | List of entries | Out |
NodeV3
h2o
string | IP | Out |
ip_port
string | IP address and port in the form a.b.c.d:e | Out |
healthy
boolean | (now-last_ping)<HeartbeatThread.TIMEOUT | Out |
last_ping
long | Time (in msec) of last ping | Out |
pid
int | PID | Out |
num_cpus
int | num_cpus | Out |
cpus_allowed
int | cpus_allowed | Out |
nthreads
int | nthreads | Out |
sys_load
float | System load; average #runnables/#cores | Out |
my_cpu_pct
int | System CPU percentage used by this H2O process in last interval | Out |
sys_cpu_pct
int | System CPU percentage used by everything in last interval | Out |
mem_value_size
long | Data on Node memory | Out |
pojo_mem
long | Temp (non Data) memory | Out |
free_mem
long | Free heap | Out |
max_mem
long | Maximum memory size for node | Out |
swap_mem
long | Size of data on node’s disk | Out |
num_keys
int | #local keys | Out |
free_disk
long | Free disk | Out |
max_disk
long | Max disk | Out |
rpcs_active
int | Active Remote Procedure Calls | Out |
fjthrds
short[] | F/J Thread count, by priority | Out |
fjqueue
short[] | F/J Task count, by priority | Out |
tcps_active
int | Open TCP connections | Out |
open_fds
int | Open File Descripters | Out |
gflops
double | Linpack GFlops | Out |
mem_bw
double | Memory Bandwidth | Out |
OutputSchemaV4
__schema
string | Url describing the schema of the current object. | In |
PCAModelOutputV3
importance
TwoDimTable | Standard deviation and importance of each principal component | In |
eigenvectors
TwoDimTable | Principal components matrix | In |
objective
double | Final value of GLRM squared loss function | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
PCAModelV3
model_id
Key | Model key | In/Out |
parameters
PCAParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
PCAOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
PCAParametersV3
transform
enum | Transformation of training data | In |
pca_method
enum | Specify the algorithm to use for computing the principal components: GramSVD - uses a distributed computation of the Gram matrix, followed by a local SVD; Power - computes the SVD using the power iteration method (experimental); Randomized - uses randomized subspace iteration method; GLRM - fits a generalized low-rank model with L2 loss function and no regularization and solves for the SVD using local matrix algebra (experimental) | In |
pca_impl
enum | Specify the implementation to use for computing PCA (via SVD or EVD): MTJ_EVD_DENSEMATRIX - eigenvalue decompositions for dense matrix using MTJ; MTJ_EVD_SYMMMATRIX - eigenvalue decompositions for symmetric matrix using MTJ; MTJ_SVD_DENSEMATRIX - singular-value decompositions for dense matrix using MTJ; JAMA - eigenvalue decompositions for dense matrix using JAMA. References: JAMA - http://math.nist.gov/javanumerics/jama/; MTJ - https://github.com/fommil/matrix-toolkits-java/ | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
k
int | Rank of matrix approximation | In/Out |
max_iterations
int | Maximum training iterations | In/Out |
seed
long | RNG seed for initialization | In/Out |
use_all_factor_levels
boolean | Whether first factor level is included in each categorical expansion | In/Out |
compute_metrics
boolean | Whether to compute metrics on the training data | In/Out |
impute_missing
boolean | Whether to impute missing entries with the column mean | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
PCAV3
parameters
PCAParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
PSVMModelOutputV3
svs_count
long | Total number of support vectors | In |
bsv_count
long | Number of bounded support vectors | In |
rho
double | rho | In |
alpha_key
Key | Weights of support vectors | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
PSVMModelV3
model_id
Key | Model key | In/Out |
parameters
PSVMParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
PSVMModelOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
PSVMParametersV3
hyper_param
double | Penalty parameter C of the error term | In |
kernel_type
enum | Type of used kernel | In |
gamma
double | Coefficient of the kernel (currently RBF gamma for gaussian kernel, -1 means 1/#features) | In |
rank_ratio
double | Desired rank of the ICF matrix expressed as an ration of number of input rows (-1 means use sqrt(#rows)). | In |
positive_weight
double | Weight of positive (+1) class of observations | In |
negative_weight
double | Weight of positive (-1) class of observations | In |
disable_training_metrics
boolean | Disable calculating training metrics (expensive on large datasets) | In |
sv_threshold
double | Threshold for accepting a candidate observation into the set of support vectors | In |
max_iterations
int | Maximum number of iteration of the algorithm | In |
fact_threshold
double | Convergence threshold of the Incomplete Cholesky Factorization (ICF) | In |
feasible_threshold
double | Convergence threshold for primal-dual residuals in the IPM iteration | In |
surrogate_gap_threshold
double | Feasibility criterion of the surrogate duality gap (eta) | In |
mu_factor
double | Increasing factor mu | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
PSVMV3
parameters
PSVMParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
ParseSVMLightV3
destination_frame
Key | Final frame name | In |
source_frames
Key[] | Source frames | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
ParseSetupV3
decrypt_tool
Key | Key-reference to an initialized instance of a Decryption Tool | In |
force_col_types
boolean | If true, will force the column types to be either the ones in Parquet schema for Parquet files or the ones specified in column_types. This parameter is used for numerical columns only. Other column settings will happen without setting this parameter. Defaults to false. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
source_frames
Key[] | Source frames | In/Out |
parse_type
enum | Parser type | In/Out |
separator
byte | Field separator | In/Out |
single_quotes
boolean | Single quotes | In/Out |
check_header
int | Check header: 0 means guess, +1 means 1st line is header not data, -1 means 1st line is data not header | In/Out |
column_names
string[] | Column names | In/Out |
skipped_columns
int[] | Skipped columns indices | In/Out |
column_types
string[] | Value types for columns | In/Out |
na_strings
string[][] | NA strings for columns | In/Out |
column_name_filter
string | Regex for names of columns to return | In/Out |
column_offset
int | Column offset to return | In/Out |
column_count
int | Number of columns to return | In/Out |
total_filtered_column_count
int | Total number of columns we would return with no column pagination | In/Out |
custom_non_data_line_markers
string | Custom characters to be treated as non-data line markers | In/Out |
partition_by
string[] | Names of the columns the persisted dataset has been partitioned by. | In/Out |
escapechar
byte | One ASCII character used to escape other characters. | In/Out |
destination_frame
string | Suggested name | Out |
header_lines
long | Number of header lines found | Out |
number_columns
int | Number of columns | Out |
data
string[][] | Sample data | Out |
warnings
string[] | Warnings | Out |
chunk_size
int | Size of individual parse tasks | Out |
ParseV3
destination_frame
Key | Final frame name | In |
source_frames
Key[] | Source frames | In |
parse_type
enum | Parser type | In |
separator
byte | Field separator | In |
single_quotes
boolean | Single Quotes | In |
check_header
int | Check header: 0 means guess, +1 means 1st line is header not data, -1 means 1st line is data not header | In |
number_columns
int | Number of columns | In |
column_names
string[] | Column names | In |
column_types
string[] | Value types for columns | In |
force_col_types
boolean | If true, will force the column types to be either the ones in Parquet schema for Parquet files or the ones specified in column_types. This parameter is used for numerical columns only. Other columnsettings will happen without setting this parameter. Defaults to false. | In |
domains
string[][] | Domains for categorical columns | In |
na_strings
string[][] | NA strings for columns | In |
chunk_size
int | Size of individual parse tasks | In |
delete_on_done
boolean | Delete input key after parse | In |
blocking
boolean | Block until the parse completes (as opposed to returning early and requiring polling | In |
decrypt_tool
Key | Key-reference to an initialized instance of a Decryption Tool | In |
custom_non_data_line_markers
string | Custom characters to be treated as non-data line markers | In |
partition_by
string[] | Name of the column the persisted dataset has been partitioned by. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
skipped_columns
int[] | Skipped columns indices | In/Out |
escapechar
byte | One ASCII character used to escape other characters. | In/Out |
job
Job | Parse job | Out |
rows
long | Rows | Out |
PartialDependenceKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
PartialDependenceV3
destination_key
Key | Key to store the destination | In |
targets
string[] | Target classes for multinomial classification | In |
model_id
Key | Model | In/Out |
frame_id
Key | Frame | In/Out |
row_index
long | Row Index | In/Out |
cols
string[] | Column(s) | In/Out |
weight_column_index
int | weight_column_index | In/Out |
add_missing_na
boolean | add_missing_na | In/Out |
nbins
int | Number of bins | In/Out |
user_splits
double[] | User define split points | In/Out |
user_cols
string[] | Column(s) of user defined splits | In/Out |
num_user_splits
int[] | Number of user defined splits per column | In/Out |
col_pairs_2dpdp
string[][] | lists of column name pairs to plot 2D pdp for | In/Out |
partial_dependence_data
TwoDimTable[] | Partial Dependence Data | Out |
PersistS3CredentialsV3
secret_key_id
string | S3 Secret Key ID | In |
secret_access_key
string | S3 Secret Key | In |
session_token
string | S3 Session token | In |
PingV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
cloud_uptime_millis
long | cloud_uptime_millis | Out |
cloud_healthy
boolean | cloud_healthy | Out |
nodes
Iced[] | nodes | Out |
PreprocessingStepDefinitionV99
type
enum | A type representing the preprocessing step to be executed. | In/Out |
ProfilerNodeEntryV3
stacktrace
string | Stack trace | Out |
count
int | Profile Count | Out |
ProfilerNodeV3
node_name
string | Node names | Out |
timestamp
long | Timestamp (millis since epoch) | Out |
entries
Iced[] | Profile entry list | Out |
ProfilerV3
depth
int | Stack trace depth | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
nodes
Iced[] | (No description available) | Out |
QuantileParametersV3
probs
double[] | Probabilities for quantiles | In |
combine_method
enum | How to combine quantiles for even sample sizes | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
QuantileV3
parameters
QuantileParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
RandomDiscreteValueSearchCriteriaV99
seed
long | Seed for random number generator; set to a value other than -1 for reproducibility. | In/Out |
max_models
int | Maximum number of models to build (optional). | In/Out |
max_runtime_secs
double | Maximum time to spend building models (optional). | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression) | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
strategy
enum | Hyperparameter space search strategy. | In/Out |
RapidsExpressionV3
name
string | (Class) name of the language construct | In |
pattern
string | Code fragment pattern. | In |
description
string | Description of the functionality provided by this language construct. | In |
RapidsFrameV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
key
Key | Frame result | Out |
num_rows
long | Rows in Frame result | Out |
num_cols
int | Columns in Frame result | Out |
RapidsFunctionV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
funstr
string | Function result | Out |
RapidsHelpV3
expressions
Iced[] | Description of the rapids language. | Out |
RapidsMapFrameV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
frames
Iced[] | Frames | Out |
map_keys
Iced | Keys of the map | Out |
RapidsNumberV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
scalar
double | Number result | Out |
RapidsNumbersV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
scalar
double[] | Number array result | Out |
RapidsSchemaV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
RapidsStringV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
string
string | String result | Out |
RapidsStringsV3
ast
string | A Rapids AstRoot expression | In |
session_id
string | Session key | In |
id
string | [DEPRECATED] Key name to assign Frame results | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
string
string[] | String array result | Out |
RapidsV99
ast
string | An Abstract Syntax Tree. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
error
string | Parsing error, if any | Out |
scalar
double | Scalar result | Out |
funstr
string | Function result | Out |
string
string | String result | Out |
key
Key | Result key | Out |
num_rows
long | Rows in Frame result | Out |
num_cols
int | Columns in Frame result | Out |
RemoveAllV3
retained_keys
Key[] | Keys of the models to retain | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
RemoveV3
key
Key | Object to be removed. | In |
cascade
boolean | If true, removal operation will cascade down the object tree. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
RequestSchemaV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
ResumeV3
recovery_dir
string | Full path to the directory with recovery data | In |
RouteV3
http_method
string | (No description available) | Out |
url_pattern
string | (No description available) | Out |
summary
string | (No description available) | Out |
api_name
string | (No description available) | Out |
handler_class
string | (No description available) | Out |
handler_method
string | (No description available) | Out |
input_schema
string | (No description available) | Out |
output_schema
string | (No description available) | Out |
path_params
string[] | (No description available) | Out |
markdown
string | (No description available) | Out |
RuleFitModelOutputV3
rule_importance
TwoDimTable | The estimated coefficients without language representations for each of the significant baselearners. | In |
intercept
double[] | Intercept. | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
RuleFitModelV3
model_id
Key | Model key | In/Out |
parameters
RuleFitParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
RuleFitOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
RuleFitParametersV3
seed
long | Seed for pseudo random number generator (if applicable). | In |
algorithm
enum | The algorithm to use to generate rules. | In |
min_rule_length
int | Minimum length of rules. Defaults to 3. | In |
max_rule_length
int | Maximum length of rules. Defaults to 3. | In |
max_num_rules
int | The maximum number of rules to return. defaults to -1 which means the number of rules is selected
by diminishing returns in model deviance. | In |
model_type
enum | Specifies type of base learners in the ensemble. | In |
rule_generation_ntrees
int | Specifies the number of trees to build in the tree model. Defaults to 50. | In |
remove_duplicates
boolean | Whether to remove rules which are identical to an earlier rule. Defaults to true. | In |
lambda
double[] | Lambda for LASSO regressor. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
RuleFitV3
parameters
RuleFitParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
SVDModelOutputV99
v_key
Key | Frame key of right singular vectors | In |
d
double[] | Singular values | In |
u_key
Key | Frame key of left singular vectors | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
SVDModelV99
model_id
Key | Model key | In/Out |
parameters
SVDParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
SVDOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
SVDParametersV99
transform
enum | Transformation of training data | In |
svd_method
enum | Method for computing SVD (Caution: Randomized is currently experimental and unstable) | In |
nv
int | Number of right singular vectors | In |
max_iterations
int | Maximum iterations | In |
seed
long | RNG seed for k-means++ initialization | In |
keep_u
boolean | Save left singular vectors? | In |
u_name
string | Frame key to save left singular vectors | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
use_all_factor_levels
boolean | Whether first factor level is included in each categorical expansion | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
SVDV99
parameters
SVDParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
SaveToHiveTableV3
frame_id
Key | H2O Frame ID | In |
jdbc_url
string | HIVE JDBC URL | In |
table_name
string | Name of table to save data to. | In |
table_path
string | HDFS Path to where the table should be stored. | In |
format
enum | Storage format of the created table. | In |
tmp_path
string | HDFS Path where to store temporary data. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
SchemaMetadataV3
version
int | Version number of the Schema. | In |
name
string | Simple name of the Schema. NOTE: the schema_names form a single namespace. | In |
superclass
string | Simple name of the superclass of the Schema. NOTE: the schema_names form a single namespace. | In |
type
string | Simple name of H2O type that this Schema represents. Must not be changed after creation (treat as final). | In |
label
string | [DEPRECATED] This field is always the same as name. | Out |
fields
FieldMetadata[] | All the public fields of the schema | Out |
markdown
string | Documentation for the schema in Markdown format with GitHub extensions | Out |
SchemaV3
(No fields)
SegmentModelsKeyV3
name
string | Name (string representation) for this Key. | In/Out |
type
string | Name (string representation) for the type of Keyed this Key points to. | In/Out |
URL
string | URL for the resource that this Key points to, if one exists. | In/Out |
SegmentModelsParametersV3
segment_models_id
Key | Uniquely identifies the collection of the segment models | In |
segments
Key | Enumeration of all segments for which to build models for | In |
segment_columns
string[] | List of columns to segment-by, models will be built for all segments in the data | In |
parallelism
int | Level of parallelism of bulk model building, it is the maximum number of models each H2O node will be building in parallel | In |
SegmentModelsV3
segment_models_id
Key | Segment Models id | In |
SequentialSearchCriteriaV99
max_models
int | Maximum number of models to build (optional). | In/Out |
max_runtime_secs
double | Maximum time to spend building models (optional). | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression) | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
early_stopping
boolean | Use early stopping | In/Out |
strategy
enum | Hyperparameter space search strategy. | In/Out |
SessionIdV4
session_key
string | Session ID | In |
__schema
string | Url describing the schema of the current object. | In |
SessionPropertyV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
session_key
string | Session ID | In/Out |
key
string | Property Key | In/Out |
value
string | Property Value | In/Out |
SharedTreeModelOutputV3
variable_importances
TwoDimTable | Variable Importances | Out |
init_f
double | The Intercept term, the initial model function value to which trees make adjustments | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
SharedTreeModelV3
model_id
Key | Model key | In/Out |
parameters
Parameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
Output | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
SharedTreeParametersV3
ntrees
int | Number of trees. | In |
max_depth
int | Maximum tree depth (0 for unlimited). | In |
min_rows
double | Fewest allowed (weighted) observations in a leaf. | In |
nbins
int | For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point | In |
nbins_top_level
int | For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level | In |
nbins_cats
int | For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. | In |
r2_stopping
double | r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
build_tree_one_node
boolean | Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. | In |
sample_rate_per_class
double[] | A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree | In |
col_sample_rate_per_tree
double | Column sample rate per tree (from 0.0 to 1.0) | In |
col_sample_rate_change_per_level
double | Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) | In |
score_tree_interval
int | Score the model after every so many trees. Disabled if set to 0. | In |
min_split_improvement
double | Minimum relative improvement in squared error reduction for a split to happen | In |
histogram_type
enum | What type of histogram to use for finding optimal split points | In |
calibrate_model
boolean | Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. | In |
in_training_checkpoints_dir
string | Create checkpoints into defined directory while training process is still running. In case of cluster shutdown, this checkpoint can be used to restart training. | In |
in_training_checkpoints_tree_interval
int | Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is defined | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
calibration_frame
Key | Data for model calibration | In/Out |
calibration_method
enum | Calibration method to use | In/Out |
check_constant_response
boolean | Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
SharedTreeV3
parameters
Parameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
ShutdownV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
SignificantRulesV3
model_id
Key | Model id of interest | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
significant_rules_table
TwoDimTable | The estimated coefficients and language representations (in case it is a rule) for each of the significant baselearners. | Out |
SplitFrameV3
key
Key | Job Key | In |
dataset
Key | Dataset | In |
ratios
double[] | Split ratios - resulting number of split is ratios.length+1 | In |
destination_frames
Key[] | Destination keys for each output frame split. | In/Out |
StackedEnsembleModelOutputV99
metalearner
Key | Model which combines the base_models into a stacked ensemble. | Out |
levelone_frame_id
Key | Level one frame used for metalearner training. | Out |
stacking_strategy
enum | The stacking strategy used for training. | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
StackedEnsembleModelV99
model_id
Key | Model key | In/Out |
parameters
StackedEnsembleParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
StackedEnsembleOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
StackedEnsembleParametersV99
keep_levelone_frame
boolean | Keep level one frame used for metalearner training. | In |
seed
long | Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number) | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
base_models
Key[] | List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be identical across models. | In/Out |
metalearner_algorithm
enum | Type of algorithm to use as the metalearner. Options include ‘AUTO’ (GLM with non negative weights; if validation_frame is present, a lambda search is performed), ‘deeplearning’ (Deep Learning with default parameters), ‘drf’ (Random Forest with default parameters), ‘gbm’ (GBM with default parameters), ‘glm’ (GLM with default parameters), ‘naivebayes’ (NaiveBayes with default parameters), or ‘xgboost’ (if available, XGBoost with default parameters). | In/Out |
metalearner_nfolds
int | Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2). | In/Out |
metalearner_fold_assignment
enum | Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is currently set to Random). The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
metalearner_fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation for cross-validation of the metalearner. | In/Out |
metalearner_transform
enum | Transformation used for the level one frame. | In/Out |
metalearner_params
string | Parameters for metalearner algorithm | In/Out |
blending_frame
Key | Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending mode if provided) | In/Out |
score_training_samples
long | Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
StackedEnsembleV99
parameters
StackedEnsembleParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
SteamMetricsV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
version
long | Steam metrics API version | Out |
idle_millis
long | Number of milliseconds that the cluster has been idle | Out |
StepDefinitionV99
name
string | Name of the step provider (usually, this is also the name of an algorithm). | In/Out |
alias
enum | An alias representing a predefined list of steps to be executed. | In/Out |
steps
Step[] | The list of steps to be executed (Mutually exclusive with alias). | In/Out |
StepV99
id
string | The id of the step (must be unique per step provider). | In/Out |
group
int | The group of execution of the given step (groups are executed in ascending order of priority).Steps with group=0 are skipped. Defaults to -1 to use the default group assigned to the step id. | In/Out |
weight
int | The relative weight for the given step (can impact time and/or number of models allocated for this step). Steps with weight=0 are skipped. Defaults to -1 to use the default weight assigned to the step id. | In/Out |
StreamingSchema
(No fields)
StringPairV3
a
string | Value A | In |
b
string | Value B | In |
TabulateV3
dataset
Key | Dataset | In |
nbins_predictor
int | Number of bins for predictor column | In |
nbins_response
int | Number of bins for response column | In |
predictor
VecSpecifier | Predictor | In/Out |
response
VecSpecifier | Response | In/Out |
weight
VecSpecifier | Observation weights (optional) | In/Out |
count_table
TwoDimTable | Counts table | Out |
response_table
TwoDimTable | Response table | Out |
TargetEncoderModelOutputV3
input_to_output_columns
ColumnsMapping[] | Mapping between input column(s) and their corresponding target encoded output column(s). Please note that there can be multiple columns on the input/from side if columns grouping was used, and there can also be multiple columns on the output/to side if the target was multiclass. | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
TargetEncoderModelV3
model_id
Key | Model key | In/Out |
parameters
TargetEncoderParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
TargetEncoderOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
TargetEncoderParametersV3
columns_to_encode
string[][] | List of categorical columns or groups of categorical columns to encode. When groups of columns are specified, each group is encoded as a single column (interactions are created internally). | In |
keep_original_categorical_columns
boolean | If true, the original non-encoded categorical features will remain in the result frame. | In |
blending
boolean | If true, enables blending of posterior probabilities (computed for a given categorical value) with prior probabilities (computed on the entire set). This allows to mitigate the effect of categorical values with small cardinality. The blending effect can be tuned using the inflection_point and smoothing parameters. | In |
inflection_point
double | Inflection point of the sigmoid used to blend probabilities (see blending parameter). For a given categorical value, if it appears less that inflection_point in a data sample, then the influence of the posterior probability will be smaller than the prior. | In |
smoothing
double | Smoothing factor corresponds to the inverse of the slope at the inflection point on the sigmoid used to blend probabilities (see blending parameter). If smoothing tends towards 0, then the sigmoid used for blending turns into a Heaviside step function. | In |
data_leakage_handling
enum | Data leakage handling strategy used to generate the encoding. Supported options are:
1) “none” (default) - no holdout, using the entire training frame.
2) “leave_one_out” - current row’s response value is subtracted from the per-level frequencies pre-calculated on the entire training frame.
3) “k_fold” - encodings for a fold are generated based on out-of-fold data. | In |
noise
double | The amount of noise to add to the encoded column. Use 0 to disable noise, and -1 (=AUTO) to let the algorithm determine a reasonable amount of noise. | In |
seed
long | Seed used to generate the noise. By default, the seed is chosen randomly. | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
TargetEncoderTransformParametersV3
model
Key | Target Encoder model to use. | In |
frame
Key | Frame to transform. | In |
as_training
boolean | Force encoding mode for training data: when using a leakage handling strategy different from None, training data should be transformed with this flag set to true (Defaults to false). | In |
blending
boolean | Enables or disables blending. Defaults to the value assigned at model creation. | In |
inflection_point
double | Inflection point. Defaults to the value assigned at model creation. | In |
smoothing
double | Smoothing. Defaults to the value assigned at model creation. | In |
noise
double | Noise. Defaults to the value assigned at model creation. | In |
TargetEncoderV3
parameters
TargetEncoderParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
TimelineV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
now
long | Current time in millis. | Out |
self
string | This node | Out |
events
Iced[] | recorded timeline events | Out |
TreeStatsV3
min_depth
int | minDepth | In |
max_depth
int | maxDepth | In |
mean_depth
float | meanDepth | In |
min_leaves
int | minLeaves | In |
max_leaves
int | maxLeaves | In |
mean_leaves
float | meanLeaves | In |
TreeV3
model
Key | Key of the model the desired tree belongs to | In |
tree_number
int | Index of the tree in the model. | In |
plain_language_rules
enum | Whether to generate plain language rules. | In |
tree_class
string | Name of the class of the tree. Ignored for regression and binomial. | In/Out |
left_children
int[] | Left child nodes in the tree | Out |
right_children
int[] | Right child nodes in the tree | Out |
root_node_id
int | Number of the root node | Out |
thresholds
float[] | Split thresholds (numeric and possibly categorical columns) | Out |
features
string[] | Names of the column of the split | Out |
nas
string[] | Which way NA Splits (LEFT, RIGHT, NA) | Out |
descriptions
string[] | Description of the tree’s nodes | Out |
levels
int[][] | Categorical levels on the edge from the parent node | Out |
predictions
float[] | Prediction values on terminal nodes | Out |
tree_decision_path
string | Plain language rules representation of a trained decision tree | Out |
decision_paths
string[] | Plain language rules that were used in a particular prediction | Out |
TwoDimTableV3
name
string | Table Name | Out |
description
string | Table Description | Out |
columns
Iced[] | Column Specification | Out |
rowcount
int | Number of Rows | Out |
data
Polymorphic[][] | Table Data (col-major) | Out |
TypeaheadV3
src
string | training_frame | In |
limit
int | limit | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
matches
string[] | matches | Out |
UnlockKeysV3
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
UpliftDRFModelOutputV3
default_auuc_thresholds
double[] | Default thresholds to calculate AUUC metric. If validation is enabled, thresholds from validation metrics is saved here. Otherwise thresholds are from training metrics. | In |
variable_importances
TwoDimTable | Variable Importances | Out |
init_f
double | The Intercept term, the initial model function value to which trees make adjustments | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
UpliftDRFModelV3
model_id
Key | Model key | In/Out |
parameters
UpliftDRFParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
UpliftDRFOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
UpliftDRFParametersV3
mtries
int | Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors | In |
sample_rate
double | Row sample rate per tree (from 0.0 to 1.0) | In |
treatment_column
string | Define the column which will be used for computing uplift gain to select best split for a tree. The column has to divide the dataset into treatment (value 1) and control (value 0) groups. | In |
uplift_metric
enum | Divergence metric used to find best split when building an uplift tree. | In |
auuc_type
enum | Metric used to calculate Area Under Uplift Curve. | In |
auuc_nbins
int | Number of bins to calculate Area Under Uplift Curve. | In |
ntrees
int | Number of trees. | In |
max_depth
int | Maximum tree depth (0 for unlimited). | In |
min_rows
double | Fewest allowed (weighted) observations in a leaf. | In |
nbins
int | For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point | In |
nbins_top_level
int | For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level | In |
nbins_cats
int | For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting. | In |
r2_stopping
double | r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and stopping_tolerance instead. Previous version of H2O would stop making trees when the R^2 metric equals or exceeds this | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
build_tree_one_node
boolean | Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. | In |
sample_rate_per_class
double[] | A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree | In |
col_sample_rate_per_tree
double | Column sample rate per tree (from 0.0 to 1.0) | In |
col_sample_rate_change_per_level
double | Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0) | In |
score_tree_interval
int | Score the model after every so many trees. Disabled if set to 0. | In |
min_split_improvement
double | Minimum relative improvement in squared error reduction for a split to happen | In |
histogram_type
enum | What type of histogram to use for finding optimal split points | In |
calibrate_model
boolean | Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. | In |
in_training_checkpoints_dir
string | Create checkpoints into defined directory while training process is still running. In case of cluster shutdown, this checkpoint can be used to restart training. | In |
in_training_checkpoints_tree_interval
int | Checkpoint the model after every so many trees. Parameter is used only when in_training_checkpoints_dir is defined | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
balance_classes
boolean | Balance training data class counts via over/under-sampling (for imbalanced data). | In/Out |
class_sampling_factors
float[] | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. | In/Out |
max_after_balance_size
float | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. | In/Out |
max_confusion_matrix_size
int | [Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs | In/Out |
calibration_frame
Key | Data for model calibration | In/Out |
calibration_method
enum | Calibration method to use | In/Out |
check_constant_response
boolean | Check if response column is constant. If enabled, then an exception is thrown if the response column is a constant value.If disabled, then model will train regardless of the response column being a constant value or not. | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
UpliftDRFV3
parameters
UpliftDRFParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
ValidationMessageV3
message_type
string | Type of validation message (ERROR, WARN, INFO, HIDE) | Out |
field_name
string | Field to which the message applies | Out |
message
string | Message text | Out |
VarImpV3
varimp
float[] | Variable importance of individual variables | Out |
names
string[] | Names of variables | Out |
WaterMeterCpuTicksV3
nodeidx
int | Index of node to query ticks for (0-based) | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
cpu_ticks
long[][] | array of tick counts per core | Out |
WaterMeterIoV3
nodeidx
int | Index of node to query ticks for (0-based) | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
persist_stats
Iced[] | array of IO info | Out |
Word2VecModelOutputV3
epochs
int | Number of epochs executed | In |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
Word2VecModelV3
model_id
Key | Model key | In/Out |
parameters
Word2VecParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
Word2VecOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
Word2VecParametersV3
vec_size
int | Set size of word vectors | In |
window_size
int | Set max skip length between words | In |
sent_sample_rate
float | Set threshold for occurrence of words. Those that appear with higher frequency in the training data
will be randomly down-sampled; useful range is (0, 1e-5) | In |
norm_model
enum | Use Hierarchical Softmax | In |
epochs
int | Number of training iterations to run | In |
min_word_freq
int | This will discard words that appear less than times | In |
init_learning_rate
float | Set the starting learning rate | In |
word_model
enum | The word model to use (SkipGram or CBOW) | In |
pre_trained
Key | Id of a data frame that contains a pre-trained (external) word2vec model | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
Word2VecSynonymsV3
model
Key | Source word2vec Model | In |
word
string | Target word to find synonyms for | In |
count
int | Number of synonyms | In |
synonyms
string[] | Synonymous words | In |
scores
double[] | Similarity scores | In |
Word2VecTransformV3
model
Key | Source word2vec Model | In |
words_frame
Key | Words Frame | In |
aggregate_method
enum | Method of aggregating word-vector sequences into a single vector | In |
vectors_frame
Key | Word Vectors Frame | Out |
Word2VecV3
parameters
Word2VecParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |
XGBoostExecReqV3
key
Key | Identifier | In |
data
string | Arbitrary request data stored as Base64 encoded binary | In |
XGBoostExecRespV3
key
Key | Identifier | In |
data
string | Arbitrary response data stored as Base64 encoded binary | In |
XGBoostModelOutputV3
variable_importances
TwoDimTable | Variable Importances | Out |
variable_importances_cover
TwoDimTable | Variable Importances - Cover | Out |
variable_importances_frequency
TwoDimTable | Variable Importances - Frequency | Out |
native_parameters
TwoDimTable | XGBoost Native Parameters | Out |
sparse
boolean | Sparse | Out |
names
string[] | Column names | Out |
original_names
string[] | Original column names | Out |
column_types
string[] | Column types | Out |
domains
string[][] | Domains for categorical columns | Out |
cross_validation_models
Key[] | Cross-validation models (model ids) | Out |
cross_validation_predictions
Key[] | Cross-validation predictions, one per cv model (deprecated, use cross_validation_holdout_predictions_frame_id instead) | Out |
cross_validation_holdout_predictions_frame_id
Key | Cross-validation holdout predictions (full out-of-sample predictions on training data) | Out |
cross_validation_fold_assignment_frame_id
Key | Cross-validation fold assignment (each row is assigned to one holdout fold) | Out |
model_category
enum | Category of the model (e.g., Binomial) | Out |
model_summary
TwoDimTable | Model summary | Out |
scoring_history
TwoDimTable | Scoring history | Out |
cv_scoring_history
TwoDimTable[] | Cross-Validation scoring history | Out |
reproducibility_information_table
TwoDimTable[] | Model reproducibility information | Out |
training_metrics
ModelMetrics | Training data model metrics | Out |
validation_metrics
ModelMetrics | Validation data model metrics | Out |
cross_validation_metrics
ModelMetrics | Cross-validation model metrics | Out |
cross_validation_metrics_summary
TwoDimTable | Cross-validation model metrics summary | Out |
status
string | Job status | Out |
start_time
long | Start time in milliseconds | Out |
end_time
long | End time in milliseconds | Out |
run_time
long | Runtime in milliseconds | Out |
default_threshold
double | Default threshold used for predictions | Out |
help
Map | Help information for output fields | Out |
XGBoostModelV3
model_id
Key | Model key | In/Out |
parameters
XGBoostParameters | The build parameters for the model (e.g. K for KMeans). | Out |
output
XGBoostOutput | The build output for the model (e.g. the cluster centers for KMeans). | Out |
compatible_frames
string[] | Compatible frames, if requested | Out |
checksum
long | Checksum for all the things that go into building the Model. | Out |
algo
string | The algo name for this Model. | Out |
algo_full_name
string | The pretty algo name for this Model (e.g., Generalized Linear Model, rather than GLM). | Out |
response_column_name
string | The response column name for this Model (if applicable). Is null otherwise. | Out |
treatment_column_name
string | The treatment column name for this Model (if applicable). Is null otherwise. | Out |
data_frame
Key | The Model’s training frame key | Out |
timestamp
long | Timestamp for when this model was completed | Out |
have_pojo
boolean | Indicator, whether export to POJO is available | Out |
have_mojo
boolean | Indicator, whether export to MOJO is available | Out |
XGBoostParametersV3
ntrees
int | (same as n_estimators) Number of trees. | In |
max_depth
int | Maximum tree depth (0 for unlimited). | In |
min_rows
double | (same as min_child_weight) Fewest allowed (weighted) observations in a leaf. | In |
min_child_weight
double | (same as min_rows) Fewest allowed (weighted) observations in a leaf. | In |
learn_rate
double | (same as eta) Learning rate (from 0.0 to 1.0) | In |
eta
double | (same as learn_rate) Learning rate (from 0.0 to 1.0) | In |
sample_rate
double | (same as subsample) Row sample rate per tree (from 0.0 to 1.0) | In |
subsample
double | (same as sample_rate) Row sample rate per tree (from 0.0 to 1.0) | In |
col_sample_rate
double | (same as colsample_bylevel) Column sample rate (from 0.0 to 1.0) | In |
colsample_bylevel
double | (same as col_sample_rate) Column sample rate (from 0.0 to 1.0) | In |
col_sample_rate_per_tree
double | (same as colsample_bytree) Column sample rate per tree (from 0.0 to 1.0) | In |
colsample_bytree
double | (same as col_sample_rate_per_tree) Column sample rate per tree (from 0.0 to 1.0) | In |
colsample_bynode
double | Column sample rate per tree node (from 0.0 to 1.0) | In |
monotone_constraints
KeyValue[] | A mapping representing monotonic constraints. Use +1 to enforce an increasing constraint and -1 to specify a decreasing constraint. | In |
max_abs_leafnode_pred
float | (same as max_delta_step) Maximum absolute value of a leaf node prediction | In |
max_delta_step
float | (same as max_abs_leafnode_pred) Maximum absolute value of a leaf node prediction | In |
score_tree_interval
int | Score the model after every so many trees. Disabled if set to 0. | In |
seed
long | Seed for pseudo random number generator (if applicable) | In |
min_split_improvement
float | (same as gamma) Minimum relative improvement in squared error reduction for a split to happen | In |
gamma
float | (same as min_split_improvement) Minimum relative improvement in squared error reduction for a split to happen | In |
nthread
int | Number of parallel threads that can be used to run XGBoost. Cannot exceed H2O cluster limits (-nthreads parameter). Defaults to maximum available | In |
build_tree_one_node
boolean | Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets. | In |
save_matrix_directory
string | Directory where to save matrices passed to XGBoost library. Useful for debugging. | In |
calibrate_model
boolean | Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities. | In |
max_bins
int | For tree_method=hist only: maximum number of bins | In |
max_leaves
int | For tree_method=hist only: maximum number of leaves | In |
tree_method
enum | Tree method | In |
grow_policy
enum | Grow policy - depthwise is standard GBM, lossguide is LightGBM | In |
booster
enum | Booster type | In |
reg_lambda
float | L2 regularization | In |
reg_alpha
float | L1 regularization | In |
quiet_mode
boolean | Enable quiet mode | In |
sample_type
enum | For booster=dart only: sample_type | In |
normalize_type
enum | For booster=dart only: normalize_type | In |
rate_drop
float | For booster=dart only: rate_drop (0..1) | In |
one_drop
boolean | For booster=dart only: one_drop | In |
skip_drop
float | For booster=dart only: skip_drop (0..1) | In |
dmatrix_type
enum | Type of DMatrix. For sparse, NAs and 0 are treated equally. | In |
backend
enum | Backend. By default (auto), a GPU is used if available. | In |
gpu_id
int[] | Which GPU(s) to use. | In |
interaction_constraints
string[][] | A set of allowed column interactions. | In |
scale_pos_weight
float | Controls the effect of observations with positive labels in relation to the observations with negative labels on gradient calculation. Useful for imbalanced problems. | In |
eval_metric
string | Specification of evaluation metric that will be passed to the native XGBoost backend. | In |
score_eval_metric_only
boolean | If enabled, score only the evaluation metric. This can make model training faster if scoring is frequent (eg. each iteration). | In |
distribution
enum | Distribution function | In |
tweedie_power
double | Tweedie power for Tweedie regression, must be between 1 and 2. | In |
quantile_alpha
double | Desired quantile for Quantile regression, must be between 0 and 1. | In |
huber_alpha
double | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). | In |
max_categorical_levels
int | For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited. | In |
calibration_frame
Key | Data for model calibration | In/Out |
calibration_method
enum | Calibration method to use | In/Out |
model_id
Key | Destination id for this model; auto-generated if not specified. | In/Out |
training_frame
Key | Id of the training data frame. | In/Out |
validation_frame
Key | Id of the validation data frame. | In/Out |
nfolds
int | Number of folds for K-fold cross-validation (0 to disable or >= 2). | In/Out |
keep_cross_validation_models
boolean | Whether to keep the cross-validation models. | In/Out |
keep_cross_validation_predictions
boolean | Whether to keep the predictions of the cross-validation models. | In/Out |
keep_cross_validation_fold_assignment
boolean | Whether to keep the cross-validation fold assignment. | In/Out |
parallelize_cross_validation
boolean | Allow parallel training of cross-validation models | In/Out |
response_column
VecSpecifier | Response variable column. | In/Out |
weights_column
VecSpecifier | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0. | In/Out |
offset_column
VecSpecifier | Offset column. This will be added to the combination of columns before applying the link function. | In/Out |
fold_column
VecSpecifier | Column with cross-validation fold index assignment per observation. | In/Out |
fold_assignment
enum | Cross-validation fold assignment scheme, if fold_column is not specified. The ‘Stratified’ option will stratify the folds based on the response variable, for classification problems. | In/Out |
categorical_encoding
enum | Encoding scheme for categorical features | In/Out |
ignored_columns
string[] | Names of columns to ignore for training. | In/Out |
ignore_const_cols
boolean | Ignore constant columns. | In/Out |
score_each_iteration
boolean | Whether to score during each iteration of model training. | In/Out |
checkpoint
Key | Model checkpoint to resume training with. | In/Out |
stopping_rounds
int | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) | In/Out |
max_runtime_secs
double | Maximum allowed runtime in seconds for model training. Use 0 to disable. | In/Out |
stopping_metric
enum | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. | In/Out |
stopping_tolerance
double | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) | In/Out |
gainslift_bins
int | Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. | In/Out |
custom_metric_func
string | Reference to custom evaluation function, format: language:keyName=funcName | In/Out |
custom_distribution_func
string | Reference to custom distribution, format: language:keyName=funcName | In/Out |
export_checkpoints_dir
string | Automatically export generated models to this directory. | In/Out |
auc_type
enum | Set default multinomial AUC type. | In/Out |
XGBoostV3
parameters
XGBoostParameters | Model builder parameters. | In |
_exclude_fields
string | Comma-separated list of JSON field paths to exclude from the result, used like: “/3/Frames?_exclude_fields=frames/frame_id/URL,__meta” | In |
algo
string | The algo name for this ModelBuilder. | Out |
algo_full_name
string | The pretty algo name for this ModelBuilder (e.g., Generalized Linear Model, rather than GLM). | Out |
can_build
enum[] | Model categories this ModelBuilder can build. | Out |
supervised
boolean | Indicator whether the model is supervised or not. | Out |
visibility
enum | Should the builder always be visible, be marked as beta, or only visible if the user starts up with the experimental flag? | Out |
job
Job | Job Key | Out |
messages
ValidationMessage[] | Parameter validation messages | Out |
error_count
int | Count of parameter validation errors | Out |
__http_status
int | HTTP status to return for this build. | Out |