org.apache.spark.ml.regression

Class RandomForestRegressor

java.lang.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, Identifiable

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

Constructors

Constructor and Description
RandomForestRegressor()
RandomForestRegressor(java.lang.String uid)

Method Summary

Methods

Modifier and Type	Method and Description
RandomForestRegressor	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Param<java.lang.String>	featuresCol() Param for features column name.
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
java.lang.String	getPredictionCol()
Param<java.lang.String>	labelCol() Param for label column name.
Param<java.lang.String>	predictionCol() Param for prediction column name.
RandomForestRegressor	setCacheNodeIds(boolean value)
RandomForestRegressor	setCheckpointInterval(int value)
RandomForestRegressor	setFeatureSubsetStrategy(java.lang.String value)
RandomForestRegressor	setImpurity(java.lang.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 java.lang.String[]	supportedFeatureSubsetStrategies() Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2
static java.lang.String[]	supportedImpurities() Accessor for supported impurity settings: variance
protected RandomForestRegressionModel	train(DataFrame dataset) Train a model using the given dataset and parameters.
java.lang.String	uid() An immutable unique ID for the object and its derivatives.
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

extractLabeledPoints, fit, setFeaturesCol, setLabelCol, setPredictionCol, transformSchema

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

fit, fit, fit, fit

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

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, 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, shouldOwn, validateParams

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

toString

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(java.lang.String uid)

RandomForestRegressor

public RandomForestRegressor()

Method Detail

supportedImpurities

public static final java.lang.String[] supportedImpurities()

Accessor for supported impurity settings: variance

supportedFeatureSubsetStrategies

public static final java.lang.String[] supportedFeatureSubsetStrategies()

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

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Returns:

(undocumented)

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(java.lang.String value)

setSubsamplingRate

public RandomForestRegressor setSubsamplingRate(double value)

setSeed

public RandomForestRegressor setSeed(long value)

setNumTrees

public RandomForestRegressor setNumTrees(int value)

setFeatureSubsetStrategy

public RandomForestRegressor setFeatureSubsetStrategy(java.lang.String value)

train

protected RandomForestRegressionModel train(DataFrame dataset)

Description copied from class: Predictor

Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

Specified by:

train in class Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

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

labelCol

public Param<java.lang.String> labelCol()

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

public Param<java.lang.String> featuresCol()

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

public Param<java.lang.String> predictionCol()

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()