Packages

class RandomForestClassificationModel extends ProbabilisticClassificationModel[Vector, RandomForestClassificationModel] with RandomForestClassifierParams with TreeEnsembleModel[DecisionTreeClassificationModel] with MLWritable with Serializable with HasTrainingSummary[RandomForestClassificationTrainingSummary]

Random Forest model for classification. It supports both binary and multiclass labels, as well as both continuous and categorical features.

Annotations
@Since( "1.4.0" )
Source
RandomForestClassifier.scala
Linear Supertypes
HasTrainingSummary[RandomForestClassificationTrainingSummary], MLWritable, TreeEnsembleModel[DecisionTreeClassificationModel], RandomForestClassifierParams, TreeClassifierParams, TreeEnsembleClassifierParams, RandomForestParams, TreeEnsembleParams, DecisionTreeParams, HasWeightCol, HasSeed, HasCheckpointInterval, ProbabilisticClassificationModel[Vector, RandomForestClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, RandomForestClassificationModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, RandomForestClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[RandomForestClassificationModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. RandomForestClassificationModel
  2. HasTrainingSummary
  3. MLWritable
  4. TreeEnsembleModel
  5. RandomForestClassifierParams
  6. TreeClassifierParams
  7. TreeEnsembleClassifierParams
  8. RandomForestParams
  9. TreeEnsembleParams
  10. DecisionTreeParams
  11. HasWeightCol
  12. HasSeed
  13. HasCheckpointInterval
  14. ProbabilisticClassificationModel
  15. ProbabilisticClassifierParams
  16. HasThresholds
  17. HasProbabilityCol
  18. ClassificationModel
  19. ClassifierParams
  20. HasRawPredictionCol
  21. PredictionModel
  22. PredictorParams
  23. HasPredictionCol
  24. HasFeaturesCol
  25. HasLabelCol
  26. Model
  27. Transformer
  28. PipelineStage
  29. Logging
  30. Params
  31. Serializable
  32. Serializable
  33. Identifiable
  34. AnyRef
  35. 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 DecisionTreeClassifier and RandomForestClassifier, Supported: "entropy" and "gini". (default = gini)

    Definition Classes
    TreeClassifierParams
  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 probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes
    HasProbabilityCol
  15. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  16. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  17. 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
  18. val thresholds: DoubleArrayParam

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  19. 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. def binarySummary: BinaryRandomForestClassificationTrainingSummary

    Gets summary of model on training set.

    Gets summary of model on training set. An exception is thrown if hasSummary is false or it is a multiclass model.

    Annotations
    @Since( "3.1.0" )
  2. final def clear(param: Param[_]): RandomForestClassificationModel.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  3. def copy(extra: ParamMap): RandomForestClassificationModel

    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
    RandomForestClassificationModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  4. def evaluate(dataset: Dataset[_]): RandomForestClassificationSummary

    Evaluates the model on a test dataset.

    Evaluates the model on a test dataset.

    dataset

    Test dataset to evaluate model on.

    Annotations
    @Since( "3.1.0" )
  5. 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
  6. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  8. 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
  9. lazy val featureImportances: Vector

    Estimate of the importance of each feature.

    Estimate of the importance of each feature.

    Each feature's importance is the average of its importance across all trees in the ensemble The importance vector is normalized to sum to 1. This method is suggested by Hastie et al. (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.) and follows the implementation from scikit-learn.

    Annotations
    @Since( "1.5.0" )
    See also

    DecisionTreeClassificationModel.featureImportances

  10. 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
  11. 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
  12. 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
  13. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  14. 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
  15. 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
  16. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  17. def hasSummary: Boolean

    Indicates whether a training summary exists for this model instance.

    Indicates whether a training summary exists for this model instance.

    Definition Classes
    HasTrainingSummary
    Annotations
    @Since( "3.0.0" )
  18. 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
  19. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  20. val numClasses: Int

    Number of classes (values which the label can take).

    Number of classes (values which the label can take).

    Definition Classes
    RandomForestClassificationModelClassificationModel
    Annotations
    @Since( "1.5.0" )
  21. val numFeatures: Int

    Returns the number of features the model was trained on.

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

    Definition Classes
    RandomForestClassificationModelPredictionModel
    Annotations
    @Since( "1.6.0" )
  22. 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.

  23. var parent: Estimator[RandomForestClassificationModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  24. def predict(features: Vector): Double

    Predict label for the given features.

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

    This default implementation for classification predicts the index of the maximum value from predictRaw().

    Definition Classes
    ClassificationModelPredictionModel
  25. def predictLeaf(features: Vector): Vector

    returns

    The indices of the leaves corresponding to the feature vector. Leaves are indexed in pre-order from 0.

    Definition Classes
    TreeEnsembleModel
  26. def predictProbability(features: Vector): Vector

    Predict the probability of each class given the features.

    Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities

    Definition Classes
    ProbabilisticClassificationModel
    Annotations
    @Since( "3.0.0" )
  27. def predictRaw(features: Vector): Vector

    Raw prediction for each possible label.

    Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

    returns

    vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

    Definition Classes
    RandomForestClassificationModelClassificationModel
    Annotations
    @Since( "3.0.0" )
  28. 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( ... )
  29. final def set[T](param: Param[T], value: T): RandomForestClassificationModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  30. def setParent(parent: Estimator[RandomForestClassificationModel]): RandomForestClassificationModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  31. def summary: RandomForestClassificationTrainingSummary

    Gets summary of model on training set.

    Gets summary of model on training set. An exception is thrown if hasSummary is false.

    Definition Classes
    RandomForestClassificationModel → HasTrainingSummary
    Annotations
    @Since( "3.1.0" )
  32. def toDebugString: String

    Full description of model

    Full description of model

    Definition Classes
    TreeEnsembleModel
  33. def toString(): String

    Summary of the model

    Summary of the model

    Definition Classes
    RandomForestClassificationModel → TreeEnsembleModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "1.4.0" )
  34. lazy val totalNumNodes: Int

    Total number of nodes, summed over all trees in the ensemble.

    Total number of nodes, summed over all trees in the ensemble.

    Definition Classes
    TreeEnsembleModel
  35. def transform(dataset: Dataset[_]): DataFrame

    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    dataset

    input dataset

    returns

    transformed dataset

    Definition Classes
    RandomForestClassificationModelProbabilisticClassificationModelClassificationModelPredictionModelTransformer
  36. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  37. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  38. final def transformImpl(dataset: Dataset[_]): DataFrame
    Definition Classes
    ClassificationModelPredictionModel
  39. 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
    RandomForestClassificationModelProbabilisticClassificationModelClassificationModelPredictionModelPipelineStage
    Annotations
    @Since( "1.4.0" )
  40. def treeWeights: Array[Double]

    Weights for each tree, zippable with trees

    Weights for each tree, zippable with trees

    Definition Classes
    RandomForestClassificationModel → TreeEnsembleModel
    Annotations
    @Since( "1.4.0" )
  41. def trees: Array[DecisionTreeClassificationModel]

    Trees in this ensemble.

    Trees in this ensemble. Warning: These have null parent Estimators.

    Definition Classes
    RandomForestClassificationModel → TreeEnsembleModel
    Annotations
    @Since( "1.4.0" )
  42. 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
    RandomForestClassificationModelIdentifiable
    Annotations
    @Since( "1.5.0" )
  43. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    RandomForestClassificationModelMLWritable
    Annotations
    @Since( "2.0.0" )

Parameter setters

  1. def setFeaturesCol(value: String): RandomForestClassificationModel

    Definition Classes
    PredictionModel
  2. final def setLeafCol(value: String): RandomForestClassificationModel.this.type

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  3. def setPredictionCol(value: String): RandomForestClassificationModel

    Definition Classes
    PredictionModel
  4. def setProbabilityCol(value: String): RandomForestClassificationModel

  5. def setRawPredictionCol(value: String): RandomForestClassificationModel

    Definition Classes
    ClassificationModel
  6. def setThresholds(value: Array[Double]): RandomForestClassificationModel

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
    TreeClassifierParams
  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 getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  16. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  17. final def getSeed: Long

    Definition Classes
    HasSeed
  18. final def getSubsamplingRate: Double

    Definition Classes
    TreeEnsembleParams
  19. def getThresholds: Array[Double]

    Definition Classes
    HasThresholds
  20. 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 getters

  1. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  2. final def getMaxMemoryInMB: Int

    Definition Classes
    DecisionTreeParams