Package org.apache.spark.ml.tree

Interface TreeEnsembleParams

All Superinterfaces:

DecisionTreeParams, HasCheckpointInterval, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, HasWeightCol, Identifiable, Params, PredictorParams, Serializable

All Known Subinterfaces:

GBTClassifierParams, GBTParams, GBTRegressorParams, RandomForestClassifierParams, RandomForestParams, RandomForestRegressorParams, TreeEnsembleClassifierParams, TreeEnsembleRegressorParams

All Known Implementing Classes:

GBTClassificationModel, GBTClassifier, GBTRegressionModel, GBTRegressor, RandomForestClassificationModel, RandomForestClassifier, RandomForestRegressionModel, RandomForestRegressor

public interface TreeEnsembleParams
extends DecisionTreeParams

Parameters for Decision Tree-based ensemble algorithms.

Note: Marked as private since this may be made public in the future.

Method Summary

Modifier and Type	Method	Description
Param<String>	featureSubsetStrategy()	The number of features to consider for splits at each tree node.
String	getFeatureSubsetStrategy()
Strategy	getOldStrategy(scala.collection.immutable.Map<Object,Object> categoricalFeatures, int numClasses, scala.Enumeration.Value oldAlgo, Impurity oldImpurity)	Create a Strategy instance to use with the old API.
double	getSubsamplingRate()
DoubleParam	subsamplingRate()	Fraction of the training data used for learning each decision tree, in range (0, 1].

Methods inherited from interface org.apache.spark.ml.tree.DecisionTreeParams

cacheNodeIds, getCacheNodeIds, getLeafCol, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getMinWeightFractionPerNode, getOldStrategy, leafCol, maxBins, maxDepth, maxMemoryInMB, minInfoGain, minInstancesPerNode, minWeightFractionPerNode, setLeafCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasCheckpointInterval

checkpointInterval, getCheckpointInterval

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasSeed

getSeed, seed

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol, weightCol

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString, uid

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copy, copyValues, defaultCopy, defaultParamMap, estimateMatadataSize, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.PredictorParams

validateAndTransformSchema

Method Details

subsamplingRate

DoubleParam subsamplingRate()

Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

Returns:

(undocumented)

getSubsamplingRate

double getSubsamplingRate()

getOldStrategy

Strategy getOldStrategy(scala.collection.immutable.Map<Object,Object> categoricalFeatures,
 int numClasses,
 scala.Enumeration.Value oldAlgo,
 Impurity oldImpurity)

Create a Strategy instance to use with the old API. NOTE: The caller should set impurity and seed.

Parameters:

categoricalFeatures - (undocumented)

numClasses - (undocumented)

oldAlgo - (undocumented)

oldImpurity - (undocumented)

Returns:

(undocumented)

featureSubsetStrategy

Param<String> featureSubsetStrategy()

The number of features to consider for splits at each tree node. Supported options: - "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees greater than 1 (forest), set to "sqrt" for classification and to "onethird" for regression. - "all": use all features - "onethird": use 1/3 of the features - "sqrt": use sqrt(number of features) - "log2": use log2(number of features) - "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

These various settings are based on the following references: - log2: tested in Breiman (2001) - sqrt: recommended by Breiman manual for random forests - The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.

Returns:

(undocumented)

See Also:

• Breiman (2001)
• Breiman manual for random forests

getFeatureSubsetStrategy

String getFeatureSubsetStrategy()