org.apache.spark.ml.classification

RandomForestClassificationModel

final class RandomForestClassificationModel extends PredictionModel[Vector, RandomForestClassificationModel] with TreeEnsembleModel with Serializable

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

Annotations
@Experimental()
Linear Supertypes
TreeEnsembleModel, 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. TreeEnsembleModel
  3. PredictionModel
  4. PredictorParams
  5. HasPredictionCol
  6. HasFeaturesCol
  7. HasLabelCol
  8. Model
  9. Transformer
  10. PipelineStage
  11. Logging
  12. Params
  13. Serializable
  14. Serializable
  15. Identifiable
  16. AnyRef
  17. Any
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Value Members

  1. final def !=(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  5. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  6. final def ==(arg0: Any): Boolean

    Definition Classes
    Any
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. 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.

    Attributes
    protected
    Definition Classes
    Params
  9. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. 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. The default implementation tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance. Subclasses should override this method if the default approach is not sufficient.

    Definition Classes
    RandomForestClassificationModelModelTransformerPipelineStageParams
  11. 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.

    to

    the target instance

    extra

    extra params to be copied

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  12. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  13. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  14. 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
  15. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance.

    Definition Classes
    Params
    See also

    explainParam()

  16. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  17. 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.

    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 < user-supplied values < extra.

    Definition Classes
    Params
  18. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  19. def featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes
    protected
    Definition Classes
    PredictionModel
  20. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  21. 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
  22. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  23. 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
  24. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  25. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  26. 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
  27. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  28. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  29. 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
  30. 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
  31. def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  32. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  33. 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
  34. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  35. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  36. def isTraceEnabled(): Boolean

    Attributes
    protected
    Definition Classes
    Logging
  37. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  38. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging
  39. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  40. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  41. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  42. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  43. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  44. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  45. def logName: String

    Attributes
    protected
    Definition Classes
    Logging
  46. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  47. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  48. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging
  49. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging
  50. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  51. final def notify(): Unit

    Definition Classes
    AnyRef
  52. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  53. val numTrees: Int

    Number of trees in ensemble

    Number of trees in ensemble

    Definition Classes
    TreeEnsembleModel
  54. 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.

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

    Definition Classes
    Params
  55. var parent: Estimator[RandomForestClassificationModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model. Note: For ensembles' component Models, this value can be null.

    Definition Classes
    Model
  56. def predict(features: Vector): Double

    Predict label for the given features.

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

    Attributes
    protected
    Definition Classes
    RandomForestClassificationModelPredictionModel
  57. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  58. final def set(paramPair: ParamPair[_]): RandomForestClassificationModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  59. final def set(param: String, value: Any): RandomForestClassificationModel.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
  60. 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.

    Attributes
    protected
    Definition Classes
    Params
  61. final def setDefault(paramPairs: ParamPair[_]*): RandomForestClassificationModel.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    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
    Annotations
    @varargs()
  62. final def setDefault[T](param: Param[T], value: T): RandomForestClassificationModel.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
  63. def setFeaturesCol(value: String): RandomForestClassificationModel

    Definition Classes
    PredictionModel
  64. 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
  65. def setPredictionCol(value: String): RandomForestClassificationModel

    Definition Classes
    PredictionModel
  66. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  67. def toDebugString: String

    Full description of model

    Full description of model

    Definition Classes
    TreeEnsembleModel
  68. def toString(): String

    Summary of the model

    Summary of the model

    Definition Classes
    RandomForestClassificationModel → TreeEnsembleModel → Identifiable → AnyRef → Any
  69. 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
  70. def transform(dataset: DataFrame): DataFrame

    Transforms dataset by reading from featuresCol, calling predict(), and storing the predictions as a new column predictionCol.

    Transforms dataset by reading from featuresCol, calling predict(), and storing the predictions as a new column predictionCol.

    dataset

    input dataset

    returns

    transformed dataset with predictionCol of type Double

    Definition Classes
    PredictionModelTransformer
  71. def transform(dataset: DataFrame, 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
  72. def transform(dataset: DataFrame, 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
    @varargs()
  73. def transformSchema(schema: StructType): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema.

    Definition Classes
    PredictionModelPipelineStage
  74. 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()
  75. def treeWeights: Array[Double]

    Weights for each tree, zippable with trees

    Weights for each tree, zippable with trees

    Definition Classes
    RandomForestClassificationModel → TreeEnsembleModel
  76. def trees: Array[DecisionTreeModel]

    Trees in this ensemble.

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

    Definition Classes
    RandomForestClassificationModel → TreeEnsembleModel
  77. 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
    RandomForestClassificationModel → Identifiable
  78. 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., org.apache.spark.mllib.linalg.VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    PredictorParams
  79. def validateParams(): Unit

    Validates parameter values stored internally.

    Validates parameter values stored internally. Raise an exception if any parameter value is invalid.

    This only needs to check for interactions between parameters. Parameter value checks which do not depend on other parameters are handled by Param.validate(). This method does not handle input/output column parameters; those are checked during schema validation.

    Definition Classes
    Params
  80. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  81. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  82. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from TreeEnsembleModel

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Transformer

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