org.apache.spark.ml.regression

Class RandomForestRegressor

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Estimator<M>

org.apache.spark.ml.Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>

org.apache.spark.ml.regression.RandomForestRegressor

All Implemented Interfaces:

java.io.Serializable, Logging, Params, HasCheckpointInterval, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, PredictorParams, DecisionTreeParams, RandomForestParams, RandomForestRegressorParams, TreeEnsembleParams, TreeRegressorParams, DefaultParamsWritable, Identifiable, MLWritable

public class RandomForestRegressor
extends Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>
implements RandomForestRegressorParams, DefaultParamsWritable

Random Forest learning algorithm for regression. It supports both continuous and categorical features.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
RandomForestRegressor()
RandomForestRegressor(String uid)

Method Summary

Modifier and Type	Method and Description
RandomForestRegressor	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static RandomForestRegressor	load(String path)
static MLReader<T>	read()
RandomForestRegressor	setCacheNodeIds(boolean value)
RandomForestRegressor	setCheckpointInterval(int value) Specifies how often to checkpoint the cached node IDs.
RandomForestRegressor	setFeatureSubsetStrategy(String value)
RandomForestRegressor	setImpurity(String value)
RandomForestRegressor	setMaxBins(int value)
RandomForestRegressor	setMaxDepth(int value)
RandomForestRegressor	setMaxMemoryInMB(int value)
RandomForestRegressor	setMinInfoGain(double value)
RandomForestRegressor	setMinInstancesPerNode(int value)
RandomForestRegressor	setNumTrees(int value)
RandomForestRegressor	setSeed(long value)
RandomForestRegressor	setSubsamplingRate(double value)
static String[]	supportedFeatureSubsetStrategies() Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
static String[]	supportedImpurities() Accessor for supported impurity settings: variance
String	uid() An immutable unique ID for the object and its derivatives.

Methods inherited from class org.apache.spark.ml.Predictor

fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

Methods inherited from class org.apache.spark.ml.Estimator

fit, fit, fit, fit

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

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

getNumTrees, numTrees

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

featureSubsetStrategy, getFeatureSubsetStrategy, getOldStrategy, getSubsamplingRate, subsamplingRate

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

cacheNodeIds, getCacheNodeIds, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getOldStrategy, maxBins, maxDepth, maxMemoryInMB, minInfoGain, minInstancesPerNode

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

validateAndTransformSchema

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

getLabelCol, labelCol

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

featuresCol, getFeaturesCol

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

getPredictionCol, predictionCol

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

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

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

toString

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

checkpointInterval, getCheckpointInterval

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

getSeed, seed

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

getImpurity, getOldImpurity, impurity

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

write

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

save

Methods inherited from interface org.apache.spark.internal.Logging

initializeLogging, initializeLogIfNecessary, initializeLogIfNecessary, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Constructor Detail

RandomForestRegressor

public RandomForestRegressor(String uid)

RandomForestRegressor

public RandomForestRegressor()

Method Detail

supportedImpurities

public static final String[] supportedImpurities()

Accessor for supported impurity settings: variance

supportedFeatureSubsetStrategies

public static final String[] supportedFeatureSubsetStrategies()

Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2

load

public static RandomForestRegressor load(String path)

read

public static MLReader<T> read()

uid

public String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setMaxDepth

public RandomForestRegressor setMaxDepth(int value)

Specified by:

setMaxDepth in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxBins

public RandomForestRegressor setMaxBins(int value)

Specified by:

setMaxBins in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMinInstancesPerNode

public RandomForestRegressor setMinInstancesPerNode(int value)

Specified by:

setMinInstancesPerNode in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMinInfoGain

public RandomForestRegressor setMinInfoGain(double value)

Specified by:

setMinInfoGain in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setMaxMemoryInMB

public RandomForestRegressor setMaxMemoryInMB(int value)

Specified by:

setMaxMemoryInMB in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setCacheNodeIds

public RandomForestRegressor setCacheNodeIds(boolean value)

Specified by:

setCacheNodeIds in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setCheckpointInterval

public RandomForestRegressor setCheckpointInterval(int value)

Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in SparkContext. Must be at least 1. (default = 10)

Specified by:

setCheckpointInterval in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setImpurity

public RandomForestRegressor setImpurity(String value)

Specified by:

setImpurity in interface TreeRegressorParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSubsamplingRate

public RandomForestRegressor setSubsamplingRate(double value)

Specified by:

setSubsamplingRate in interface TreeEnsembleParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSeed

public RandomForestRegressor setSeed(long value)

Specified by:

setSeed in interface DecisionTreeParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setNumTrees

public RandomForestRegressor setNumTrees(int value)

Specified by:

setNumTrees in interface RandomForestParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

setFeatureSubsetStrategy

public RandomForestRegressor setFeatureSubsetStrategy(String value)

Specified by:

setFeatureSubsetStrategy in interface TreeEnsembleParams

Parameters:

value - (undocumented)

Returns:

(undocumented)

copy

public RandomForestRegressor copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)