org.apache.spark.ml.regression

Class GBTRegressor

Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<Vector,GBTRegressor,GBTRegressionModel>

org.apache.spark.ml.regression.GBTRegressor

All Implemented Interfaces:

java.io.Serializable, Logging, Params

public final class GBTRegressor
extends Predictor<Vector,GBTRegressor,GBTRegressionModel>
implements Logging

:: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for regression. It supports both continuous and categorical features.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
GBTRegressor()
GBTRegressor(String uid)

Method Summary

Methods

Modifier and Type	Method and Description
String	getLossType()
Param<String>	lossType() Loss function which GBT tries to minimize.
GBTRegressor	setCacheNodeIds(boolean value)
GBTRegressor	setCheckpointInterval(int value)
GBTRegressor	setImpurity(String value) The impurity setting is ignored for GBT models.
GBTRegressor	setLossType(String value)
GBTRegressor	setMaxBins(int value)
GBTRegressor	setMaxDepth(int value)
GBTRegressor	setMaxIter(int value)
GBTRegressor	setMaxMemoryInMB(int value)
GBTRegressor	setMinInfoGain(double value)
GBTRegressor	setMinInstancesPerNode(int value)
GBTRegressor	setSeed(long value)
GBTRegressor	setStepSize(double value)
GBTRegressor	setSubsamplingRate(double value)
static String[]	supportedLossTypes() Accessor for supported loss settings: squared (L2), absolute (L1)
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

copy, 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.Logging

initializeIfNecessary, initializeLogging, isTraceEnabled, log_, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning

Methods inherited from interface org.apache.spark.ml.param.Params

clear, copyValues, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, paramMap, params, set, set, set, setDefault, setDefault, setDefault, shouldOwn, validateParams

Constructor Detail

GBTRegressor

public GBTRegressor(String uid)

GBTRegressor

public GBTRegressor()

Method Detail

supportedLossTypes

public static final String[] supportedLossTypes()

Accessor for supported loss settings: squared (L2), absolute (L1)

uid

public String uid()

setMaxDepth

public GBTRegressor setMaxDepth(int value)

setMaxBins

public GBTRegressor setMaxBins(int value)

setMinInstancesPerNode

public GBTRegressor setMinInstancesPerNode(int value)

setMinInfoGain

public GBTRegressor setMinInfoGain(double value)

setMaxMemoryInMB

public GBTRegressor setMaxMemoryInMB(int value)

setCacheNodeIds

public GBTRegressor setCacheNodeIds(boolean value)

setCheckpointInterval

public GBTRegressor setCheckpointInterval(int value)

setImpurity

public GBTRegressor setImpurity(String value)

The impurity setting is ignored for GBT models. Individual trees are built using impurity "Variance."

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSubsamplingRate

public GBTRegressor setSubsamplingRate(double value)

setSeed

public GBTRegressor setSeed(long value)

setMaxIter

public GBTRegressor setMaxIter(int value)

setStepSize

public GBTRegressor setStepSize(double value)

lossType

public Param<String> lossType()

Loss function which GBT tries to minimize. (case-insensitive) Supported: "squared" (L2) and "absolute" (L1) (default = squared)

Returns:

(undocumented)

setLossType

public GBTRegressor setLossType(String value)

getLossType

public String getLossType()

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