org.apache.spark.ml.regression

Class RandomForestRegressionModel

java.lang.Object

org.apache.spark.ml.PipelineStage

org.apache.spark.ml.Transformer

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

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

org.apache.spark.ml.regression.RandomForestRegressionModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params, Identifiable

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

:: Experimental :: Random Forest model for regression. It supports both continuous and categorical features. param: _trees Decision trees in the ensemble. param: numFeatures Number of features used by this model

See Also:

Serialized Form

Method Summary

Methods

Modifier and Type	Method and Description
RandomForestRegressionModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
Vector	featureImportances() Estimate of the importance of each feature.
Param<java.lang.String>	featuresCol() Param for features column name.
static RandomForestRegressionModel	fromOld(RandomForestModel oldModel, RandomForestRegressor parent, scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeatures, int numFeatures) (private[ml]) Convert a model from the old API
java.lang.String	getFeaturesCol()
java.lang.String	getLabelCol()
java.lang.String	getPredictionCol()
Param<java.lang.String>	labelCol() Param for label column name.
int	numFeatures() Returns the number of features the model was trained on.
protected double	predict(Vector features) Predict label for the given features.
Param<java.lang.String>	predictionCol() Param for prediction column name.
java.lang.String	toString()
protected DataFrame	transformImpl(DataFrame dataset)
org.apache.spark.ml.tree.DecisionTreeModel[]	trees()
double[]	treeWeights()
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.PredictionModel

featuresDataType, 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 org.apache.spark.ml.PipelineStage

transformSchema

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, 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.Logging

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

Method Detail

fromOld

public static RandomForestRegressionModel fromOld(RandomForestModel oldModel,
                                  RandomForestRegressor parent,
                                  scala.collection.immutable.Map<java.lang.Object,java.lang.Object> categoricalFeatures,
                                  int numFeatures)

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

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)

numFeatures

public int numFeatures()

Description copied from class: PredictionModel

Returns the number of features the model was trained on. If unknown, returns -1

Overrides:

numFeatures in class PredictionModel<Vector,RandomForestRegressionModel>

trees

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

treeWeights

public double[] treeWeights()

transformImpl

protected DataFrame transformImpl(DataFrame dataset)

Overrides:

transformImpl in class PredictionModel<Vector,RandomForestRegressionModel>

predict

protected double predict(Vector features)

Description copied from class: PredictionModel

Predict label for the given features. This internal method is used to implement transform() and output predictionCol.

Specified by:

predict in class PredictionModel<Vector,RandomForestRegressionModel>

Parameters:

features - (undocumented)

Returns:

(undocumented)

copy

public RandomForestRegressionModel 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 Model<RandomForestRegressionModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

toString

public java.lang.String toString()

Specified by:

toString in interface Identifiable

Overrides:

toString in class java.lang.Object

featureImportances

public Vector featureImportances()

Estimate of the importance of each feature.

This generalizes the idea of "Gini" importance to other losses, following the explanation of Gini importance from "Random Forests" documentation by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.

This feature importance is calculated as follows: - Average over trees: - importance(feature j) = sum (over nodes which split on feature j) of the gain, where gain is scaled by the number of instances passing through node - Normalize importances for tree based on total number of training instances used to build tree. - Normalize feature importance vector to sum to 1.

Returns:

(undocumented)

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

labelCol

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

Param for label column name.

Returns:

(undocumented)

getLabelCol

public java.lang.String getLabelCol()

featuresCol

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

Param for features column name.

Returns:

(undocumented)

getFeaturesCol

public java.lang.String getFeaturesCol()

predictionCol

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

Param for prediction column name.

Returns:

(undocumented)

getPredictionCol

public java.lang.String getPredictionCol()