org.apache.spark.ml.regression

Class GBTRegressionModel

Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

org.apache.spark.ml.PredictionModel<Vector,GBTRegressionModel>

org.apache.spark.ml.regression.GBTRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params

public final class GBTRegressionModel
extends PredictionModel<Vector,GBTRegressionModel>
implements scala.Serializable

:: Experimental ::

Gradient-Boosted Trees (GBTs) model for regression. It supports both continuous and categorical features. param: _trees Decision trees in the ensemble. param: _treeWeights Weights for the decision trees in the ensemble.

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
GBTRegressionModel(String uid, DecisionTreeRegressionModel[] _trees, double[] _treeWeights)

Method Summary

Methods

Modifier and Type	Method and Description
GBTRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static GBTRegressionModel	fromOld(GradientBoostedTreesModel oldModel, GBTRegressor parent, scala.collection.immutable.Map<Object,Object> categoricalFeatures) (private[ml]) Convert a model from the old API
String	toString()
org.apache.spark.ml.tree.DecisionTreeModel[]	trees()
double[]	treeWeights()
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.PredictionModel

setFeaturesCol, setPredictionCol, transform, transformSchema

Methods inherited from class org.apache.spark.ml.Model

hasParent, parent, setParent

Methods inherited from class org.apache.spark.ml.Transformer

transform, transform, transform

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

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

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

GBTRegressionModel

public GBTRegressionModel(String uid,
                  DecisionTreeRegressionModel[] _trees,
                  double[] _treeWeights)

Method Detail

fromOld

public static GBTRegressionModel fromOld(GradientBoostedTreesModel oldModel,
                         GBTRegressor parent,
                         scala.collection.immutable.Map<Object,Object> categoricalFeatures)

(private[ml]) Convert a model from the old API

uid

public String uid()

trees

public org.apache.spark.ml.tree.DecisionTreeModel[] trees()

treeWeights

public double[] treeWeights()

copy

public GBTRegressionModel copy(ParamMap extra)

Description copied from interface: Params

Creates a copy of this instance with the same UID and some extra params. The default implementation tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance. Subclasses should override this method if the default approach is not sufficient.

Specified by:

copy in interface Params

Overrides:

copy in class Model<GBTRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

toString

public String toString()

Overrides:

toString in class Object

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