org.apache.spark.ml.regression

Class DecisionTreeRegressor

java.lang.Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<Vector,DecisionTreeRegressor,DecisionTreeRegressionModel>

org.apache.spark.ml.regression.DecisionTreeRegressor

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public final class DecisionTreeRegressor
extends Predictor<Vector,DecisionTreeRegressor,DecisionTreeRegressionModel>

:: Experimental :: Decision tree learning algorithm for regression. It supports both continuous and categorical features.

See Also:

Serialized Form

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
DecisionTreeRegressor	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
DecisionTreeRegressor	setCacheNodeIds(boolean value)
DecisionTreeRegressor	setCheckpointInterval(int value)
DecisionTreeRegressor	setImpurity(java.lang.String value)
DecisionTreeRegressor	setMaxBins(int value)
DecisionTreeRegressor	setMaxDepth(int value)
DecisionTreeRegressor	setMaxMemoryInMB(int value)
DecisionTreeRegressor	setMinInfoGain(double value)
DecisionTreeRegressor	setMinInstancesPerNode(int value)
static java.lang.String[]	supportedImpurities() Accessor for supported impurities: variance
protected DecisionTreeRegressionModel	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

DecisionTreeRegressor

public DecisionTreeRegressor(java.lang.String uid)

DecisionTreeRegressor

public DecisionTreeRegressor()

Method Detail

supportedImpurities

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

Accessor for supported impurities: variance

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 DecisionTreeRegressor setMaxDepth(int value)

setMaxBins

public DecisionTreeRegressor setMaxBins(int value)

setMinInstancesPerNode

public DecisionTreeRegressor setMinInstancesPerNode(int value)

setMinInfoGain

public DecisionTreeRegressor setMinInfoGain(double value)

setMaxMemoryInMB

public DecisionTreeRegressor setMaxMemoryInMB(int value)

setCacheNodeIds

public DecisionTreeRegressor setCacheNodeIds(boolean value)

setCheckpointInterval

public DecisionTreeRegressor setCheckpointInterval(int value)

setImpurity

public DecisionTreeRegressor setImpurity(java.lang.String value)

train

protected DecisionTreeRegressionModel 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,DecisionTreeRegressor,DecisionTreeRegressionModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

copy

public DecisionTreeRegressor 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,DecisionTreeRegressor,DecisionTreeRegressionModel>

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