Parameters of H2OIsolationForest

Affected Class

  • ai.h2o.sparkling.ml.algos.H2OIsolationForest

Parameters

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

calibrationDataFrame

Calibration frame for Platt Scaling. To enable usage of the data frame, set the parameter calibrateModel to True.

Scala default value: null ; Python default value: None

buildTreeOneNode

Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.

Scala default value: false ; Python default value: False

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.

colSampleRateChangePerLevel

Relative change of the column sampling rate for every level (must be > 0.0 and <= 2.0).

Default value: 1.0

Also available on the trained model.

colSampleRatePerTree

Column sample rate per tree (from 0.0 to 1.0).

Default value: 1.0

Also available on the trained model.

columnsToCategorical

List of columns to convert to categorical before modelling

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

contamination

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).

Default value: -1.0

Also available on the trained model.

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.

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.

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.

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

maxDepth

Maximum tree depth (0 for unlimited).

Default value: 8

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.

minRows

Fewest allowed (weighted) observations in a leaf.

Default value: 1.0

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

mtries

Number of variables randomly sampled as candidates at each split. If set to -1, defaults (number of predictors)/3.

Default value: -1

Also available on the trained model.

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.

ntrees

Number of trees.

Default value: 50

Also available on the trained model.

predictionCol

Prediction column name

Default value: "prediction"

Also available on the trained model.

sampleRate

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.

Default value: -1.0

Also available on the trained model.

sampleSize

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.

Scala default value: 256L ; Python default value: 256

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.

scoreTreeInterval

Score the model after every so many trees. Disabled if set to 0.

Default value: 0

Also available on the trained model.

seed

Seed for pseudo random number generator (if applicable).

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: 0.01

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

validationLabelCol

(experimental) Name of the label column in the validation data frame. The label column should be a string column with two distinct values indicating the anomaly. The negative value must be alphabetically smaller than the positive value. (E.g. ‘0’/’1’, ‘False’/’True’

Default value: "label"

withContributions

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

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.