Packages

class RandomForestRegressor extends Regressor[Vector, RandomForestRegressor, RandomForestRegressionModel] with RandomForestRegressorParams with DefaultParamsWritable

Random Forest learning algorithm for regression. It supports both continuous and categorical features.

Annotations
@Since( "1.4.0" )
Source
RandomForestRegressor.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, RandomForestRegressorParams, TreeRegressorParams, HasVarianceImpurity, TreeEnsembleRegressorParams, RandomForestParams, TreeEnsembleParams, DecisionTreeParams, HasWeightCol, HasSeed, HasCheckpointInterval, Regressor[Vector, RandomForestRegressor, RandomForestRegressionModel], Predictor[Vector, RandomForestRegressor, RandomForestRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[RandomForestRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. RandomForestRegressor
  2. DefaultParamsWritable
  3. MLWritable
  4. RandomForestRegressorParams
  5. TreeRegressorParams
  6. HasVarianceImpurity
  7. TreeEnsembleRegressorParams
  8. RandomForestParams
  9. TreeEnsembleParams
  10. DecisionTreeParams
  11. HasWeightCol
  12. HasSeed
  13. HasCheckpointInterval
  14. Regressor
  15. Predictor
  16. PredictorParams
  17. HasPredictionCol
  18. HasFeaturesCol
  19. HasLabelCol
  20. Estimator
  21. PipelineStage
  22. Logging
  23. Params
  24. Serializable
  25. Serializable
  26. Identifiable
  27. AnyRef
  28. Any
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Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val checkpointInterval: IntParam

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1).

    Param for set checkpoint interval (>= 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.

    Definition Classes
    HasCheckpointInterval
  2. final val featureSubsetStrategy: Param[String]

    The number of features to consider for splits at each tree node.

    The number of features to consider for splits at each tree node. Supported options:

    • "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees greater than 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
    • "all": use all features
    • "onethird": use 1/3 of the features
    • "sqrt": use sqrt(number of features)
    • "log2": use log2(number of features)
    • "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

    These various settings are based on the following references:

    • log2: tested in Breiman (2001)
    • sqrt: recommended by Breiman manual for random forests
    • The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.
    Definition Classes
    TreeEnsembleParams
    See also

    Breiman (2001)

    Breiman manual for random forests

  3. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  4. final val impurity: Param[String]

    Criterion used for information gain calculation (case-insensitive).

    Criterion used for information gain calculation (case-insensitive). This impurity type is used in DecisionTreeRegressor, RandomForestRegressor, GBTRegressor and GBTClassifier (since GBTClassificationModel is internally composed of DecisionTreeRegressionModels). Supported: "variance". (default = variance)

    Definition Classes
    HasVarianceImpurity
  5. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  6. final val leafCol: Param[String]

    Leaf indices column name.

    Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  7. final val maxBins: IntParam

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be at least 2 and at least number of categories in any categorical feature. (default = 32)

    Definition Classes
    DecisionTreeParams
  8. final val maxDepth: IntParam

    Maximum depth of the tree (nonnegative).

    Maximum depth of the tree (nonnegative). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)

    Definition Classes
    DecisionTreeParams
  9. final val minInfoGain: DoubleParam

    Minimum information gain for a split to be considered at a tree node.

    Minimum information gain for a split to be considered at a tree node. Should be at least 0.0. (default = 0.0)

    Definition Classes
    DecisionTreeParams
  10. final val minInstancesPerNode: IntParam

    Minimum number of instances each child must have after split.

    Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Must be at least 1. (default = 1)

    Definition Classes
    DecisionTreeParams
  11. final val minWeightFractionPerNode: DoubleParam

    Minimum fraction of the weighted sample count that each child must have after split.

    Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in the interval [0.0, 0.5). (default = 0.0)

    Definition Classes
    DecisionTreeParams
  12. final val numTrees: IntParam

    Number of trees to train (at least 1).

    Number of trees to train (at least 1). If 1, then no bootstrapping is used. If greater than 1, then bootstrapping is done. TODO: Change to always do bootstrapping (simpler). SPARK-7130 (default = 20)

    Note: The reason that we cannot add this to both GBT and RF (i.e. in TreeEnsembleParams) is the param maxIter controls how many trees a GBT has. The semantics in the algorithms are a bit different.

    Definition Classes
    RandomForestParams
  13. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  14. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  15. final val subsamplingRate: DoubleParam

    Fraction of the training data used for learning each decision tree, in range (0, 1].

    Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

    Definition Classes
    TreeEnsembleParams
  16. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol

Members

  1. final def clear(param: Param[_]): RandomForestRegressor.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  2. def copy(extra: ParamMap): RandomForestRegressor

    Creates a copy of this instance with the same UID and some extra 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().

    Definition Classes
    RandomForestRegressorPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  3. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  4. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  6. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  7. def fit(dataset: Dataset[_]): RandomForestRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  8. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[RandomForestRegressionModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], paramMap: ParamMap): RandomForestRegressionModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): RandomForestRegressionModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  11. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  12. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  13. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  14. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  15. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  16. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  17. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  18. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  19. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  20. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  21. final def set[T](param: Param[T], value: T): RandomForestRegressor.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  22. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  23. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    PredictorPipelineStage
  24. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    RandomForestRegressorIdentifiable
    Annotations
    @Since( "1.4.0" )
  25. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Parameter setters

  1. def setBootstrap(value: Boolean): RandomForestRegressor.this.type

    Annotations
    @Since( "3.0.0" )
  2. def setCheckpointInterval(value: Int): RandomForestRegressor.this.type

    Specifies how often to checkpoint the cached node IDs.

    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 org.apache.spark.SparkContext. Must be at least 1. (default = 10)

    Annotations
    @Since( "1.4.0" )
  3. def setFeatureSubsetStrategy(value: String): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  4. def setFeaturesCol(value: String): RandomForestRegressor

    Definition Classes
    Predictor
  5. def setImpurity(value: String): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  6. def setLabelCol(value: String): RandomForestRegressor

    Definition Classes
    Predictor
  7. final def setLeafCol(value: String): RandomForestRegressor.this.type

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  8. def setMaxBins(value: Int): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  9. def setMaxDepth(value: Int): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  10. def setMinInfoGain(value: Double): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  11. def setMinInstancesPerNode(value: Int): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  12. def setMinWeightFractionPerNode(value: Double): RandomForestRegressor.this.type

    Annotations
    @Since( "3.0.0" )
  13. def setNumTrees(value: Int): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  14. def setPredictionCol(value: String): RandomForestRegressor

    Definition Classes
    Predictor
  15. def setSeed(value: Long): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  16. def setSubsamplingRate(value: Double): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  17. def setWeightCol(value: String): RandomForestRegressor.this.type

    Sets the value of param weightCol.

    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.

    Annotations
    @Since( "3.0.0" )

Parameter getters

  1. final def getBootstrap: Boolean

    Definition Classes
    RandomForestParams
    Annotations
    @Since( "3.0.0" )
  2. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  3. final def getFeatureSubsetStrategy: String

    Definition Classes
    TreeEnsembleParams
  4. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  5. final def getImpurity: String

    Definition Classes
    HasVarianceImpurity
  6. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  7. final def getLeafCol: String

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  8. final def getMaxBins: Int

    Definition Classes
    DecisionTreeParams
  9. final def getMaxDepth: Int

    Definition Classes
    DecisionTreeParams
  10. final def getMinInfoGain: Double

    Definition Classes
    DecisionTreeParams
  11. final def getMinInstancesPerNode: Int

    Definition Classes
    DecisionTreeParams
  12. final def getMinWeightFractionPerNode: Double

    Definition Classes
    DecisionTreeParams
  13. final def getNumTrees: Int

    Definition Classes
    RandomForestParams
  14. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  15. final def getSeed: Long

    Definition Classes
    HasSeed
  16. final def getSubsamplingRate: Double

    Definition Classes
    TreeEnsembleParams
  17. final def getWeightCol: String

    Definition Classes
    HasWeightCol

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

  1. final val bootstrap: BooleanParam

    Whether bootstrap samples are used when building trees.

    Whether bootstrap samples are used when building trees.

    Definition Classes
    RandomForestParams
    Annotations
    @Since( "3.0.0" )
  2. final val cacheNodeIds: BooleanParam

    If false, the algorithm will pass trees to executors to match instances with nodes.

    If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default = false)

    Definition Classes
    DecisionTreeParams
  3. final val maxMemoryInMB: IntParam

    Maximum memory in MB allocated to histogram aggregation.

    Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default = 256 MB)

    Definition Classes
    DecisionTreeParams

(expert-only) Parameter setters

  1. def setCacheNodeIds(value: Boolean): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  2. def setMaxMemoryInMB(value: Int): RandomForestRegressor.this.type

    Annotations
    @Since( "1.4.0" )

(expert-only) Parameter getters

  1. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  2. final def getMaxMemoryInMB: Int

    Definition Classes
    DecisionTreeParams