Packages

class RandomForestClassifier extends ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel] with RandomForestClassifierParams with DefaultParamsWritable

Random Forest learning algorithm 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
DefaultParamsWritable, MLWritable, RandomForestClassifierParams, TreeClassifierParams, TreeEnsembleClassifierParams, RandomForestParams, TreeEnsembleParams, DecisionTreeParams, HasWeightCol, HasSeed, HasCheckpointInterval, ProbabilisticClassifier[Vector, RandomForestClassifier, RandomForestClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, RandomForestClassifier, RandomForestClassificationModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, RandomForestClassifier, RandomForestClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[RandomForestClassificationModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. RandomForestClassifier
  2. DefaultParamsWritable
  3. MLWritable
  4. RandomForestClassifierParams
  5. TreeClassifierParams
  6. TreeEnsembleClassifierParams
  7. RandomForestParams
  8. TreeEnsembleParams
  9. DecisionTreeParams
  10. HasWeightCol
  11. HasSeed
  12. HasCheckpointInterval
  13. ProbabilisticClassifier
  14. ProbabilisticClassifierParams
  15. HasThresholds
  16. HasProbabilityCol
  17. Classifier
  18. ClassifierParams
  19. HasRawPredictionCol
  20. Predictor
  21. PredictorParams
  22. HasPredictionCol
  23. HasFeaturesCol
  24. HasLabelCol
  25. Estimator
  26. PipelineStage
  27. Logging
  28. Params
  29. Serializable
  30. Serializable
  31. Identifiable
  32. AnyRef
  33. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new RandomForestClassifier()
    Annotations
    @Since( "1.4.0" )
  2. new RandomForestClassifier(uid: String)
    Annotations
    @Since( "1.4.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. 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" )
  7. 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
  8. 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
  9. final def clear(param: Param[_]): RandomForestClassifier.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  10. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  11. def copy(extra: ParamMap): RandomForestClassifier

    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
    RandomForestClassifierPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "1.4.1" )
  12. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  13. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  16. 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
  17. def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes
    Params
  18. def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    ClassifierParams
  19. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  20. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  21. def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    dataset

    DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
    Note

    Throws SparkException if any label is a non-integer or is negative

  22. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  23. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  24. 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
  25. 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

  26. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  27. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  28. def fit(dataset: Dataset[_]): RandomForestClassificationModel

    Fits a model to the input data.

    Fits a model to the input data.

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

    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" )
  30. def fit(dataset: Dataset[_], paramMap: ParamMap): RandomForestClassificationModel

    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" )
  31. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): RandomForestClassificationModel

    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()
  32. 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
  33. final def getBootstrap: Boolean

    Definition Classes
    RandomForestParams
    Annotations
    @Since( "3.0.0" )
  34. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  35. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  36. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  37. 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
  38. final def getFeatureSubsetStrategy: String

    Definition Classes
    TreeEnsembleParams
  39. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  40. final def getImpurity: String

    Definition Classes
    TreeClassifierParams
  41. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  42. final def getLeafCol: String

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

    Definition Classes
    DecisionTreeParams
  44. final def getMaxDepth: Int

    Definition Classes
    DecisionTreeParams
  45. final def getMaxMemoryInMB: Int

    Definition Classes
    DecisionTreeParams
  46. final def getMinInfoGain: Double

    Definition Classes
    DecisionTreeParams
  47. final def getMinInstancesPerNode: Int

    Definition Classes
    DecisionTreeParams
  48. final def getMinWeightFractionPerNode: Double

    Definition Classes
    DecisionTreeParams
  49. def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int

    Get the number of classes.

    Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.

    Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().

    dataset

    Dataset which contains a column labelCol

    maxNumClasses

    Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.

    returns

    number of classes

    Attributes
    protected
    Definition Classes
    Classifier
    Exceptions thrown

    IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses

  50. final def getNumTrees: Int

    Definition Classes
    RandomForestParams
  51. 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
  52. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  53. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  54. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  55. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  56. final def getSeed: Long

    Definition Classes
    HasSeed
  57. final def getSubsamplingRate: Double

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

    Definition Classes
    HasThresholds
  59. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  60. 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
  61. 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
  62. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  63. 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
  64. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  65. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. 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
  67. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  68. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  69. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  70. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  71. 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" )
  72. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  73. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  80. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. 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
  85. 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
  86. 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
  87. 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
  88. 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
  89. 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
  90. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  91. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  92. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  93. 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
  94. 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.

  95. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  96. 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
  97. 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
  98. 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( ... )
  99. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  100. final def set(paramPair: ParamPair[_]): RandomForestClassifier.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  101. final def set(param: String, value: Any): RandomForestClassifier.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  102. final def set[T](param: Param[T], value: T): RandomForestClassifier.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  103. def setBootstrap(value: Boolean): RandomForestClassifier.this.type

    Annotations
    @Since( "3.0.0" )
  104. def setCacheNodeIds(value: Boolean): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  105. def setCheckpointInterval(value: Int): RandomForestClassifier.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" )
  106. final def setDefault(paramPairs: ParamPair[_]*): RandomForestClassifier.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  107. final def setDefault[T](param: Param[T], value: T): RandomForestClassifier.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  108. def setFeatureSubsetStrategy(value: String): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  109. def setFeaturesCol(value: String): RandomForestClassifier

    Definition Classes
    Predictor
  110. def setImpurity(value: String): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  111. def setLabelCol(value: String): RandomForestClassifier

    Definition Classes
    Predictor
  112. final def setLeafCol(value: String): RandomForestClassifier.this.type

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

    Annotations
    @Since( "1.4.0" )
  114. def setMaxDepth(value: Int): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  115. def setMaxMemoryInMB(value: Int): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  116. def setMinInfoGain(value: Double): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  117. def setMinInstancesPerNode(value: Int): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  118. def setMinWeightFractionPerNode(value: Double): RandomForestClassifier.this.type

    Annotations
    @Since( "3.0.0" )
  119. def setNumTrees(value: Int): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  120. def setPredictionCol(value: String): RandomForestClassifier

    Definition Classes
    Predictor
  121. def setProbabilityCol(value: String): RandomForestClassifier

    Definition Classes
    ProbabilisticClassifier
  122. def setRawPredictionCol(value: String): RandomForestClassifier

    Definition Classes
    Classifier
  123. def setSeed(value: Long): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  124. def setSubsamplingRate(value: Double): RandomForestClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  125. def setThresholds(value: Array[Double]): RandomForestClassifier

    Definition Classes
    ProbabilisticClassifier
  126. def setWeightCol(value: String): RandomForestClassifier.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" )
  127. 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
  128. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  129. 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
  130. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  131. def train(dataset: Dataset[_]): RandomForestClassificationModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    RandomForestClassifierPredictor
  132. 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
  133. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  134. 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
    RandomForestClassifierIdentifiable
    Annotations
    @Since( "1.4.0" )
  135. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    TreeEnsembleClassifierParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  136. def validateLabel(label: Double, numClasses: Int): Unit

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    label

    The label to validate.

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
  137. def validateNumClasses(numClasses: Int): Unit

    Validates that number of classes is greater than zero.

    Validates that number of classes is greater than zero.

    numClasses

    Number of classes label can take.

    Attributes
    protected
    Definition Classes
    Classifier
  138. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  139. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  140. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  141. 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
  142. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from RandomForestClassifierParams

Inherited from TreeClassifierParams

Inherited from TreeEnsembleClassifierParams

Inherited from RandomForestParams

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasWeightCol

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

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

Members

Parameter setters

Parameter getters

(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.

(expert-only) Parameter setters

(expert-only) Parameter getters