org.apache.spark.ml.classification
Class RandomForestClassificationModel

Object
  org.apache.spark.ml.PipelineStage
      org.apache.spark.ml.Transformer
          org.apache.spark.ml.Model<M>
              org.apache.spark.ml.PredictionModel<Vector,RandomForestClassificationModel>
                  org.apache.spark.ml.classification.RandomForestClassificationModel

All Implemented Interfaces:

java.io.Serializable, Logging, Params

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

:: Experimental :: Random Forest model for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features. param: _trees Decision trees in the ensemble. Warning: These have null parents.

See Also:

Serialized Form

Method Summary

RandomForestClassificationModel	copy(ParamMap extra) Creates a copy of this instance with the same UID and some extra params.
static RandomForestClassificationModel	fromOld(RandomForestModel oldModel, RandomForestClassifier 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, defaultCopy, 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

Method Detail

fromOld

public static RandomForestClassificationModel fromOld(RandomForestModel oldModel,
                                                      RandomForestClassifier 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 RandomForestClassificationModel 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<RandomForestClassificationModel>

Parameters:

extra - (undocumented)

Returns:

(undocumented)

See Also:

defaultCopy()

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