Parameters of H2OStackedEnsemble

Affected Class

• ai.h2o.sparkling.ml.algos.H2OStackedEnsemble

Parameters

• Each parameter has also a corresponding getter and setter method. (E.g.: label -> getLabel() , setLabel(...) )

baseAlgorithms

An array of base algorithms

Scala default value: null ; Python default value: None

blendingDataFrame

This parameter is used for computing the predictions that serve as the training frame for the meta-learner. If provided, this triggers blending mode on the stacked ensemble training stage. Blending mode is faster than cross-validating the base learners (though these ensembles may not perform as well as the Super Learner ensemble). The parameter is not serializable!

Scala default value: null ; Python default value: None

aucType

Set default multinomial AUC type. Possible values are "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO".

Default value: "AUTO"

Also available on the trained model.

categoricalEncoding

Encoding scheme for categorical features. Possible values are "AUTO", "OneHotInternal", "OneHotExplicit", "Enum", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited".

Default value: "AUTO"

Also available on the trained model.

checkpoint

Model checkpoint to resume training with.

Scala default value: null ; Python default value: None

Also available on the trained model.

columnsToCategorical

List of columns to convert to categorical before modelling

Scala default value: Array() ; Python default value: []

convertInvalidNumbersToNa

If set to ‘true’, the model converts invalid numbers to NA during making predictions.

Scala default value: false ; Python default value: False

Also available on the trained model.

convertUnknownCategoricalLevelsToNa

If set to ‘true’, the model converts unknown categorical levels to NA during making predictions.

Scala default value: false ; Python default value: False

Also available on the trained model.

customDistributionFunc

Reference to custom distribution, format: language:keyName=funcName.

Scala default value: null ; Python default value: None

Also available on the trained model.

customMetricFunc

Reference to custom evaluation function, format: language:keyName=funcName.

Scala default value: null ; Python default value: None

Also available on the trained model.

dataFrameSerializer

A full name of a serializer used for serialization and deserialization of Spark DataFrames to a JSON value within NullableDataFrameParam.

Default value: "ai.h2o.sparkling.utils.JSONDataFrameSerializer"

Also available on the trained model.

detailedPredictionCol

Column containing additional prediction details, its content depends on the model type.

Default value: "detailed_prediction"

Also available on the trained model.

distribution

Distribution function. Possible values are "AUTO", "bernoulli", "quasibinomial", "modified_huber", "multinomial", "ordinal", "gaussian", "poisson", "gamma", "tweedie", "huber", "laplace", "quantile", "fractionalbinomial", "negativebinomial", "custom".

Default value: "AUTO"

Also available on the trained model.

exportCheckpointsDir

Automatically export generated models to this directory.

Scala default value: null ; Python default value: None

Also available on the trained model.

featuresCols

Name of feature columns

Scala default value: Array() ; Python default value: []

Also available on the trained model.

foldAssignment

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. Possible values are "AUTO", "Random", "Modulo", "Stratified".

Default value: "AUTO"

Also available on the trained model.

foldCol

Column with cross-validation fold index assignment per observation.

Scala default value: null ; Python default value: None

Also available on the trained model.

gainsliftBins

Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning.

Default value: -1

Also available on the trained model.

huberAlpha

Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1).

Default value: 0.9

Also available on the trained model.

ignoreConstCols

Ignore constant columns.

Scala default value: true ; Python default value: True

Also available on the trained model.

ignoredCols

Names of columns to ignore for training.

Scala default value: null ; Python default value: None

Also available on the trained model.

keepBinaryModels

If set to true, all binary models created during execution of the fit method will be kept in DKV of H2O-3 cluster.

Scala default value: false ; Python default value: False

keepCrossValidationFoldAssignment

Whether to keep the cross-validation fold assignment.

Scala default value: false ; Python default value: False

Also available on the trained model.

keepCrossValidationModels

Whether to keep the cross-validation models.

Scala default value: true ; Python default value: True

Also available on the trained model.

keepCrossValidationPredictions

Whether to keep the predictions of the cross-validation models.

Scala default value: false ; Python default value: False

Also available on the trained model.

keepLeveloneFrame

Keep level one frame used for metalearner training.

Scala default value: false ; Python default value: False

Also available on the trained model.

labelCol

Response variable column.

Scala default value: null ; Python default value: None

Also available on the trained model.

maxCategoricalLevels

For every categorical feature, only use this many most frequent categorical levels for model training. Only used for categorical_encoding == EnumLimited.

Default value: 10

Also available on the trained model.

maxRuntimeSecs

Maximum allowed runtime in seconds for model training. Use 0 to disable.

Default value: 0.0

Also available on the trained model.

metalearnerAlgorithm

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). Possible values are "AUTO", "deeplearning", "drf", "gbm", "glm", "naivebayes", "xgboost".

Default value: "AUTO"

Also available on the trained model.

metalearnerFoldAssignment

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. Possible values are "AUTO", "Random", "Modulo", "Stratified".

Default value: "AUTO"

Also available on the trained model.

metalearnerFoldCol

Column with cross-validation fold index assignment per observation for cross-validation of the metalearner.

Scala default value: null ; Python default value: None

Also available on the trained model.

metalearnerNfolds

Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2).

Default value: 0

Also available on the trained model.

metalearnerParams

Parameters for metalearner algorithm.

Default value: ""

Also available on the trained model.

metalearnerTransform

Transformation used for the level one frame. Possible values are "NONE", "Logit".

Default value: "NONE"

Also available on the trained model.

modelId

Destination id for this model; auto-generated if not specified.

Scala default value: null ; Python default value: None

namedMojoOutputColumns

Mojo Output is not stored in the array but in the properly named columns

Scala default value: true ; Python default value: True

Also available on the trained model.

nfolds

Number of folds for K-fold cross-validation (0 to disable or >= 2).

Default value: 0

Also available on the trained model.

offsetCol

Offset column. This will be added to the combination of columns before applying the link function.

Scala default value: null ; Python default value: None

Also available on the trained model.

parallelizeCrossValidation

Allow parallel training of cross-validation models.

Scala default value: true ; Python default value: True

predictionCol

Prediction column name

Default value: "prediction"

Also available on the trained model.

quantileAlpha

Desired quantile for Quantile regression, must be between 0 and 1.

Default value: 0.5

Also available on the trained model.

scoreEachIteration

Whether to score during each iteration of model training.

Scala default value: false ; Python default value: False

Also available on the trained model.

scoreTrainingSamples

Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples, enter 0.

Scala default value: 10000L ; Python default value: 10000

Also available on the trained model.

seed

Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number).

Scala default value: -1L ; Python default value: -1

Also available on the trained model.

splitRatio

Accepts values in range [0, 1.0] which determine how large part of dataset is used for training and for validation. For example, 0.8 -> 80% training 20% validation. This parameter is ignored when validationDataFrame is set.

Default value: 1.0

stoppingMetric

Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anonomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Possible values are "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "anomaly_score", "custom", "custom_increasing".

Default value: "AUTO"

Also available on the trained model.

stoppingRounds

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).

Default value: 0

Also available on the trained model.

stoppingTolerance

Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much).

Default value: 0.001

Also available on the trained model.

tweediePower

Tweedie power for Tweedie regression, must be between 1 and 2.

Default value: 1.5

Also available on the trained model.

validationDataFrame

A data frame dedicated for a validation of the trained model. If the parameters is not set,a validation frame created via the ‘splitRatio’ parameter. The parameter is not serializable!

Scala default value: null ; Python default value: None

weightCol

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.

Scala default value: null ; Python default value: None

Also available on the trained model.

withContributions

Enables or disables generating a sub-column of detailedPredictionCol containing Shapley values of original features.

Scala default value: false ; Python default value: False

Also available on the trained model.

withLeafNodeAssignments

Enables or disables computation of leaf node assignments.

Scala default value: false ; Python default value: False

Also available on the trained model.

withStageResults

Enables or disables computation of stage results.

Scala default value: false ; Python default value: False

Also available on the trained model.