org.apache.spark.ml.regression
Class DecisionTreeRegressor

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

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

DecisionTreeRegressor()

DecisionTreeRegressor(String uid)

Method Summary

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(String value)
DecisionTreeRegressor	setMaxBins(int value)
DecisionTreeRegressor	setMaxDepth(int value)
DecisionTreeRegressor	setMaxMemoryInMB(int value)
DecisionTreeRegressor	setMinInfoGain(double value)
DecisionTreeRegressor	setMinInstancesPerNode(int value)
static String[]	supportedImpurities() Accessor for supported impurities: 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

DecisionTreeRegressor

public DecisionTreeRegressor(String uid)

DecisionTreeRegressor

public DecisionTreeRegressor()

Method Detail

supportedImpurities

public static final String[] supportedImpurities()

Accessor for supported impurities: variance

uid

public String uid()

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(String value)

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