org.apache.spark.ml.classification

Class RandomForestClassifier

java.lang.Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<FeaturesType,E,M>

org.apache.spark.ml.classification.Classifier<FeaturesType,E,M>

org.apache.spark.ml.classification.ProbabilisticClassifier<Vector,RandomForestClassifier,RandomForestClassificationModel>

org.apache.spark.ml.classification.RandomForestClassifier

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public final class RandomForestClassifier
extends ProbabilisticClassifier<Vector,RandomForestClassifier,RandomForestClassificationModel>

:: Experimental :: Random Forest learning algorithm for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

See Also:

Serialized Form

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
RandomForestClassifier	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()
java.lang.String	getRawPredictionCol()
Param<java.lang.String>	labelCol() Param for label column name.
Param<java.lang.String>	predictionCol() Param for prediction column name.
Param<java.lang.String>	rawPredictionCol() Param for raw prediction (a.k.a.
RandomForestClassifier	setCacheNodeIds(boolean value)
RandomForestClassifier	setCheckpointInterval(int value)
RandomForestClassifier	setFeatureSubsetStrategy(java.lang.String value)
RandomForestClassifier	setImpurity(java.lang.String value)
RandomForestClassifier	setMaxBins(int value)
RandomForestClassifier	setMaxDepth(int value)
RandomForestClassifier	setMaxMemoryInMB(int value)
RandomForestClassifier	setMinInfoGain(double value)
RandomForestClassifier	setMinInstancesPerNode(int value)
RandomForestClassifier	setNumTrees(int value)
RandomForestClassifier	setSeed(long value)
RandomForestClassifier	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: entropy, gini
protected RandomForestClassificationModel	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)
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.classification.ProbabilisticClassifier

setProbabilityCol, setThresholds

Methods inherited from class org.apache.spark.ml.classification.Classifier

setRawPredictionCol

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

RandomForestClassifier

public RandomForestClassifier(java.lang.String uid)

RandomForestClassifier

public RandomForestClassifier()

Method Detail

supportedImpurities

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

Accessor for supported impurity settings: entropy, gini

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 RandomForestClassifier setMaxDepth(int value)

setMaxBins

public RandomForestClassifier setMaxBins(int value)

setMinInstancesPerNode

public RandomForestClassifier setMinInstancesPerNode(int value)

setMinInfoGain

public RandomForestClassifier setMinInfoGain(double value)

setMaxMemoryInMB

public RandomForestClassifier setMaxMemoryInMB(int value)

setCacheNodeIds

public RandomForestClassifier setCacheNodeIds(boolean value)

setCheckpointInterval

public RandomForestClassifier setCheckpointInterval(int value)

setImpurity

public RandomForestClassifier setImpurity(java.lang.String value)

setSubsamplingRate

public RandomForestClassifier setSubsamplingRate(double value)

setSeed

public RandomForestClassifier setSeed(long value)

setNumTrees

public RandomForestClassifier setNumTrees(int value)

setFeatureSubsetStrategy

public RandomForestClassifier setFeatureSubsetStrategy(java.lang.String value)

train

protected RandomForestClassificationModel 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,RandomForestClassifier,RandomForestClassificationModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

copy

public RandomForestClassifier 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,RandomForestClassifier,RandomForestClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

validateAndTransformSchema

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

rawPredictionCol

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

Param for raw prediction (a.k.a. confidence) column name.

Returns:

(undocumented)

getRawPredictionCol

public java.lang.String getRawPredictionCol()

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()