Parameters of H2ODRF

Affected Classes

  • ai.h2o.sparkling.ml.algos.H2ODRF

  • ai.h2o.sparkling.ml.algos.classification.H2ODRFClassifier

  • ai.h2o.sparkling.ml.algos.regression.H2ODRFRegressor

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

ignoredCols

Names of columns to ignore for training.

Scala default value: null ; Python default value: None

Also available on the trained model.

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.

balanceClasses

Balance training data class counts via over/under-sampling (for imbalanced data).

Scala default value: false ; Python default value: False

Also available on the trained model.

binomialDoubleTrees

For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.

Scala default value: false ; Python default value: False

Also available on the trained model.

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.

calibrateModel

Use Platt Scaling (default) or Isotonic Regression to calculate calibrated class probabilities. Calibration can provide more accurate estimates of class probabilities.

Scala default value: false ; Python default value: False

Also available on the trained model.

calibrationMethod

Calibration method to use. Possible values are "AUTO", "PlattScaling", "IsotonicRegression".

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.

checkConstantResponse

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.

Scala default value: true ; Python default value: True

Also available on the trained model.

classSamplingFactors

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.

Scala default value: null ; Python default value: None

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: []

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.

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.

histogramType

What type of histogram to use for finding optimal split points. Possible values are "AUTO", "UniformAdaptive", "Random", "QuantilesGlobal", "RoundRobin", "UniformRobust".

Default value: "AUTO"

Also available on the trained model.

ignoreConstCols

Ignore constant columns.

Scala default value: true ; Python default value: True

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.

labelCol

Response variable column.

Default value: "label"

Also available on the trained model.

maxAfterBalanceSize

Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.

Scala default value: 5.0f ; Python default value: 5.0

Also available on the trained model.

maxConfusionMatrixSize

[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.

Default value: 20

Also available on the trained model.

maxDepth

Maximum tree depth (0 for unlimited).

Default value: 20

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.

minSplitImprovement

Minimum relative improvement in squared error reduction for a split to happen.

Scala default value: 1.0e-5 ; Python default value: 1.0E-5

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 to sqrt{p} for classification and p/3 for regression (where p is the # of predictors.

Default value: -1

Also available on the trained model.

nbins

For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point.

Default value: 20

Also available on the trained model.

nbinsCats

For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.

Default value: 1024

Also available on the trained model.

nbinsTopLevel

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.

Default value: 1024

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.

ntrees

Number of trees.

Default value: 50

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.

predictionCol

Prediction column name

Default value: "prediction"

Also available on the trained model.

sampleRate

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

Default value: 0.632

Also available on the trained model.

sampleRatePerClass

A list of row sample rates per class (relative fraction for each class, from 0.0 to 1.0), for each tree.

Scala default value: null ; Python default value: None

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 anomaly_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", "AUUC", "ATE", "ATT", "ATC", "qini", "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.

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.