org.apache.spark.ml.classification

Class DecisionTreeClassifier

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,DecisionTreeClassifier,DecisionTreeClassificationModel>

org.apache.spark.ml.classification.DecisionTreeClassifier

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

public final class DecisionTreeClassifier
extends ProbabilisticClassifier<Vector,DecisionTreeClassifier,DecisionTreeClassificationModel>

:: Experimental :: Decision tree 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
DecisionTreeClassifier()
DecisionTreeClassifier(java.lang.String uid)

Method Summary

Methods

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

DecisionTreeClassifier

public DecisionTreeClassifier(java.lang.String uid)

DecisionTreeClassifier

public DecisionTreeClassifier()

Method Detail

supportedImpurities

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

Accessor for supported impurities: entropy, gini

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 DecisionTreeClassifier setMaxDepth(int value)

setMaxBins

public DecisionTreeClassifier setMaxBins(int value)

setMinInstancesPerNode

public DecisionTreeClassifier setMinInstancesPerNode(int value)

setMinInfoGain

public DecisionTreeClassifier setMinInfoGain(double value)

setMaxMemoryInMB

public DecisionTreeClassifier setMaxMemoryInMB(int value)

setCacheNodeIds

public DecisionTreeClassifier setCacheNodeIds(boolean value)

setCheckpointInterval

public DecisionTreeClassifier setCheckpointInterval(int value)

setImpurity

public DecisionTreeClassifier setImpurity(java.lang.String value)

train

protected DecisionTreeClassificationModel 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,DecisionTreeClassifier,DecisionTreeClassificationModel>

Parameters:

dataset - Training dataset

Returns:

Fitted model

copy

public DecisionTreeClassifier 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,DecisionTreeClassifier,DecisionTreeClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

validateAndTransformSchema

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

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