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

public final class RandomForestRegressor
extends Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>

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

See Also:

Serialized Form

Constructor Summary

RandomForestRegressor()

RandomForestRegressor(String uid)

Method Summary

RandomForestRegressor	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
RandomForestRegressor	setCacheNodeIds(boolean value)
RandomForestRegressor	setCheckpointInterval(int value)
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()
StructType	validateAndTransformSchema(StructType schema, boolean fitting, DataType featuresDataType) Validates and transforms the input schema with the provided param map.

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.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, setDefault, shouldOwn, validateParams

Methods inherited from interface org.apache.spark.Logging

initializeIfNecessary, initializeLogging, 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

uid

public String uid()

setMaxDepth

public RandomForestRegressor setMaxDepth(int value)

setMaxBins

public RandomForestRegressor setMaxBins(int value)

setMinInstancesPerNode

public RandomForestRegressor setMinInstancesPerNode(int value)

setMinInfoGain

public RandomForestRegressor setMinInfoGain(double value)

setMaxMemoryInMB

public RandomForestRegressor setMaxMemoryInMB(int value)

setCacheNodeIds

public RandomForestRegressor setCacheNodeIds(boolean value)

setCheckpointInterval

public RandomForestRegressor setCheckpointInterval(int value)

setImpurity

public RandomForestRegressor setImpurity(String value)

setSubsamplingRate

public RandomForestRegressor setSubsamplingRate(double value)

setSeed

public RandomForestRegressor setSeed(long value)

setNumTrees

public RandomForestRegressor setNumTrees(int value)

setFeatureSubsetStrategy

public RandomForestRegressor setFeatureSubsetStrategy(String value)

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.

Specified by:

copy in interface Params

Specified by:

copy in class Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

validateAndTransformSchema

public StructType validateAndTransformSchema(StructType schema,
                                             boolean fitting,
                                             DataType featuresDataType)

Validates and transforms the input schema with the provided param map.

Parameters:

schema - input schema

fitting - whether this is in fitting

featuresDataType - SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

Returns:

output schema