Package org.apache.spark.ml.regression

Class RandomForestRegressor

Object
org.apache.spark.ml.PipelineStage
org.apache.spark.ml.Estimator<M>
org.apache.spark.ml.Predictor<FeaturesType,Learner,M>
org.apache.spark.ml.regression.Regressor<Vector,RandomForestRegressor,RandomForestRegressionModel>
org.apache.spark.ml.regression.RandomForestRegressor
All Implemented Interfaces:
Serializable, org.apache.spark.internal.Logging, Params, HasCheckpointInterval, HasFeaturesCol, HasLabelCol, HasPredictionCol, HasSeed, HasWeightCol, org.apache.spark.ml.PredictorParams, org.apache.spark.ml.tree.DecisionTreeParams, org.apache.spark.ml.tree.HasVarianceImpurity, org.apache.spark.ml.tree.RandomForestParams, org.apache.spark.ml.tree.RandomForestRegressorParams, org.apache.spark.ml.tree.TreeEnsembleParams, org.apache.spark.ml.tree.TreeEnsembleRegressorParams, org.apache.spark.ml.tree.TreeRegressorParams, DefaultParamsWritable, Identifiable, MLWritable
public class RandomForestRegressor extends Regressor<Vector,RandomForestRegressor,RandomForestRegressionModel> implements org.apache.spark.ml.tree.RandomForestRegressorParams, DefaultParamsWritable
Random Forest learning algorithm for regression. It supports both continuous and categorical features.
See Also:

  • Constructor Details

    • RandomForestRegressor

      public RandomForestRegressor(String uid)

    • RandomForestRegressor

      public RandomForestRegressor()

  • Method Details

    • supportedImpurities

      public static final String[] supportedImpurities()
      Accessor for supported impurity settings: variance

    • supportedFeatureSubsetStrategies

      public static final String[] supportedFeatureSubsetStrategies()
      Accessor for supported featureSubsetStrategy settings: auto, all, onethird, sqrt, log2

    • load

      public static RandomForestRegressor load(String path)

    • read

      public static MLReader<T> read()

    • impurity

      public final Param<String> impurity()
      Specified by:
      impurity in interface org.apache.spark.ml.tree.HasVarianceImpurity

    • numTrees

      public final IntParam numTrees()
      Specified by:
      numTrees in interface org.apache.spark.ml.tree.RandomForestParams

    • bootstrap

      public final BooleanParam bootstrap()
      Specified by:
      bootstrap in interface org.apache.spark.ml.tree.RandomForestParams

    • subsamplingRate

      public final DoubleParam subsamplingRate()
      Specified by:
      subsamplingRate in interface org.apache.spark.ml.tree.TreeEnsembleParams

    • featureSubsetStrategy

      public final Param<String> featureSubsetStrategy()
      Specified by:
      featureSubsetStrategy in interface org.apache.spark.ml.tree.TreeEnsembleParams

    • leafCol

      public final Param<String> leafCol()
      Specified by:
      leafCol in interface org.apache.spark.ml.tree.DecisionTreeParams

    • maxDepth

      public final IntParam maxDepth()
      Specified by:
      maxDepth in interface org.apache.spark.ml.tree.DecisionTreeParams

    • maxBins

      public final IntParam maxBins()
      Specified by:
      maxBins in interface org.apache.spark.ml.tree.DecisionTreeParams

    • minInstancesPerNode

      public final IntParam minInstancesPerNode()
      Specified by:
      minInstancesPerNode in interface org.apache.spark.ml.tree.DecisionTreeParams

    • minWeightFractionPerNode

      public final DoubleParam minWeightFractionPerNode()
      Specified by:
      minWeightFractionPerNode in interface org.apache.spark.ml.tree.DecisionTreeParams

    • minInfoGain

      public final DoubleParam minInfoGain()
      Specified by:
      minInfoGain in interface org.apache.spark.ml.tree.DecisionTreeParams

    • maxMemoryInMB

      public final IntParam maxMemoryInMB()
      Specified by:
      maxMemoryInMB in interface org.apache.spark.ml.tree.DecisionTreeParams

    • cacheNodeIds

      public final BooleanParam cacheNodeIds()
      Specified by:
      cacheNodeIds in interface org.apache.spark.ml.tree.DecisionTreeParams

    • weightCol

      public final Param<String> weightCol()
      Description copied from interface: HasWeightCol
      Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
      Specified by:
      weightCol in interface HasWeightCol
      Returns:
      (undocumented)

    • seed

      public final LongParam seed()
      Description copied from interface: HasSeed
      Param for random seed.
      Specified by:
      seed in interface HasSeed
      Returns:
      (undocumented)

    • checkpointInterval

      public final IntParam checkpointInterval()
      Description copied from interface: HasCheckpointInterval
      Param for set checkpoint interval (&gt;= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.
      Specified by:
      checkpointInterval in interface HasCheckpointInterval
      Returns:
      (undocumented)

    • uid

      public 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 RandomForestRegressor setMaxDepth(int value)

    • setMaxBins

      public RandomForestRegressor setMaxBins(int value)

    • setMinInstancesPerNode

      public RandomForestRegressor setMinInstancesPerNode(int value)

    • setMinWeightFractionPerNode

      public RandomForestRegressor setMinWeightFractionPerNode(double value)

    • setMinInfoGain

      public RandomForestRegressor setMinInfoGain(double value)

    • setMaxMemoryInMB

      public RandomForestRegressor setMaxMemoryInMB(int value)

    • setCacheNodeIds

      public RandomForestRegressor setCacheNodeIds(boolean value)

    • setCheckpointInterval

      public RandomForestRegressor setCheckpointInterval(int value)
      Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in SparkContext. Must be at least 1. (default = 10)
      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • setImpurity

      public RandomForestRegressor setImpurity(String value)

    • setSubsamplingRate

      public RandomForestRegressor setSubsamplingRate(double value)

    • setSeed

      public RandomForestRegressor setSeed(long value)

    • setNumTrees

      public RandomForestRegressor setNumTrees(int value)

    • setBootstrap

      public RandomForestRegressor setBootstrap(boolean value)

    • setFeatureSubsetStrategy

      public RandomForestRegressor setFeatureSubsetStrategy(String value)

    • setWeightCol

      public RandomForestRegressor setWeightCol(String value)
      Sets the value of param weightCol(). If this is not set or empty, we treat all instance weights as 1.0. By default the weightCol is not set, so all instances have weight 1.0.

      Parameters:
      value - (undocumented)
      Returns:
      (undocumented)

    • copy

      public RandomForestRegressor 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. See defaultCopy().
      Specified by:
      copy in interface Params
      Specified by:
      copy in class Predictor<Vector,RandomForestRegressor,RandomForestRegressionModel>
      Parameters:
      extra - (undocumented)
      Returns:
      (undocumented)