org.apache.spark.ml.classification

Class GBTClassifier

java.lang.Object

org.apache.spark.ml.PipelineStage

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

org.apache.spark.ml.Predictor<Vector,GBTClassifier,GBTClassificationModel>

org.apache.spark.ml.classification.GBTClassifier

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public final class GBTClassifier
extends Predictor<Vector,GBTClassifier,GBTClassificationModel>
implements Logging

:: Experimental :: Gradient-Boosted Trees (GBTs) learning algorithm for classification. It supports binary labels, as well as both continuous and categorical features. Note: Multiclass labels are not currently supported.

See Also:

Serialized Form

Constructor Summary

Constructors

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

Method Summary

Methods

Modifier and Type	Method and Description
GBTClassifier	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
java.lang.String	getLossType()
Param<java.lang.String>	lossType() Loss function which GBT tries to minimize.
GBTClassifier	setCacheNodeIds(boolean value)
GBTClassifier	setCheckpointInterval(int value)
GBTClassifier	setImpurity(java.lang.String value) The impurity setting is ignored for GBT models.
GBTClassifier	setLossType(java.lang.String value)
GBTClassifier	setMaxBins(int value)
GBTClassifier	setMaxDepth(int value)
GBTClassifier	setMaxIter(int value)
GBTClassifier	setMaxMemoryInMB(int value)
GBTClassifier	setMinInfoGain(double value)
GBTClassifier	setMinInstancesPerNode(int value)
GBTClassifier	setSeed(long value)
GBTClassifier	setStepSize(double value)
GBTClassifier	setSubsamplingRate(double value)
static java.lang.String[]	supportedLossTypes() Accessor for supported loss settings: logistic
protected GBTClassificationModel	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.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, 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

Constructor Detail

GBTClassifier

public GBTClassifier(java.lang.String uid)

GBTClassifier

public GBTClassifier()

Method Detail

supportedLossTypes

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

Accessor for supported loss settings: logistic

uid

public java.lang.String uid()

Description copied from interface: Identifiable

An immutable unique ID for the object and its derivatives.

Specified by:

uid in interface Identifiable

Returns:

(undocumented)

setMaxDepth

public GBTClassifier setMaxDepth(int value)

setMaxBins

public GBTClassifier setMaxBins(int value)

setMinInstancesPerNode

public GBTClassifier setMinInstancesPerNode(int value)

setMinInfoGain

public GBTClassifier setMinInfoGain(double value)

setMaxMemoryInMB

public GBTClassifier setMaxMemoryInMB(int value)

setCacheNodeIds

public GBTClassifier setCacheNodeIds(boolean value)

setCheckpointInterval

public GBTClassifier setCheckpointInterval(int value)

setImpurity

public GBTClassifier setImpurity(java.lang.String value)

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

Parameters:

value - (undocumented)

Returns:

(undocumented)

setSubsamplingRate

public GBTClassifier setSubsamplingRate(double value)

setSeed

public GBTClassifier setSeed(long value)

setMaxIter

public GBTClassifier setMaxIter(int value)

setStepSize

public GBTClassifier setStepSize(double value)

lossType

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

Loss function which GBT tries to minimize. (case-insensitive) Supported: "logistic" (default = logistic)

Returns:

(undocumented)

setLossType

public GBTClassifier setLossType(java.lang.String value)

getLossType

public java.lang.String getLossType()

train

protected GBTClassificationModel 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,GBTClassifier,GBTClassificationModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

copy

public GBTClassifier 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,GBTClassifier,GBTClassificationModel>

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