Class/Object

org.apache.spark.mllib.tree.model

GradientBoostedTreesModel

Related Docs: object GradientBoostedTreesModel | package model

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class GradientBoostedTreesModel extends TreeEnsembleModel with Saveable

Represents a gradient boosted trees model.

Annotations
@Since( "1.2.0" )
Source
treeEnsembleModels.scala
Linear Supertypes
Saveable, TreeEnsembleModel, Serializable, Serializable, AnyRef, Any
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Inherited
  1. GradientBoostedTreesModel
  2. Saveable
  3. TreeEnsembleModel
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new GradientBoostedTreesModel(algo: Algo, trees: Array[DecisionTreeModel], treeWeights: Array[Double])

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    algo

    algorithm for the ensemble model, either Classification or Regression

    trees

    tree ensembles

    treeWeights

    tree ensemble weights

    Annotations
    @Since( "1.2.0" )

Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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

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    Definition Classes
    AnyRef → Any
  4. val algo: Algo

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    algorithm for the ensemble model, either Classification or Regression

    algorithm for the ensemble model, either Classification or Regression

    Definition Classes
    GradientBoostedTreesModel → TreeEnsembleModel
    Annotations
    @Since( "1.2.0" )
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  7. val combiningStrategy: EnsembleCombiningStrategy

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    Attributes
    protected
    Definition Classes
    TreeEnsembleModel
  8. final def eq(arg0: AnyRef): Boolean

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

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    Definition Classes
    AnyRef → Any
  10. def evaluateEachIteration(data: RDD[LabeledPoint], loss: Loss): Array[Double]

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    Method to compute error or loss for every iteration of gradient boosting.

    Method to compute error or loss for every iteration of gradient boosting.

    data

    RDD of org.apache.spark.mllib.regression.LabeledPoint

    loss

    evaluation metric.

    returns

    an array with index i having the losses or errors for the ensemble containing the first i+1 trees

    Annotations
    @Since( "1.4.0" )
  11. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. def formatVersion: String

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    Current version of model save/load format.

    Current version of model save/load format.

    Attributes
    protected
    Definition Classes
    GradientBoostedTreesModelSaveable
  13. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  14. def hashCode(): Int

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

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    Definition Classes
    Any
  16. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  19. def numTrees: Int

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    Get number of trees in ensemble.

    Get number of trees in ensemble.

    Definition Classes
    TreeEnsembleModel
  20. def predict(features: JavaRDD[Vector]): JavaRDD[Double]

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    Java-friendly version of org.apache.spark.mllib.tree.model.TreeEnsembleModel#predict.

    Java-friendly version of org.apache.spark.mllib.tree.model.TreeEnsembleModel#predict.

    Definition Classes
    TreeEnsembleModel
  21. def predict(features: RDD[Vector]): RDD[Double]

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    Predict values for the given data set.

    Predict values for the given data set.

    features

    RDD representing data points to be predicted

    returns

    RDD[Double] where each entry contains the corresponding prediction

    Definition Classes
    TreeEnsembleModel
  22. def predict(features: Vector): Double

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    Predict values for a single data point using the model trained.

    Predict values for a single data point using the model trained.

    features

    array representing a single data point

    returns

    predicted category from the trained model

    Definition Classes
    TreeEnsembleModel
  23. def save(sc: SparkContext, path: String): Unit

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    sc

    Spark context used to save model data.

    path

    Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.

    Definition Classes
    GradientBoostedTreesModelSaveable
    Annotations
    @Since( "1.3.0" )
  24. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  25. def toDebugString: String

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    Print the full model to a string.

    Print the full model to a string.

    Definition Classes
    TreeEnsembleModel
  26. def toString(): String

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    Print a summary of the model.

    Print a summary of the model.

    Definition Classes
    TreeEnsembleModel → AnyRef → Any
  27. def totalNumNodes: Int

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

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

    Definition Classes
    TreeEnsembleModel
  28. val treeWeights: Array[Double]

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    tree ensemble weights

    tree ensemble weights

    Definition Classes
    GradientBoostedTreesModel → TreeEnsembleModel
    Annotations
    @Since( "1.2.0" )
  29. val trees: Array[DecisionTreeModel]

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    tree ensembles

    tree ensembles

    Definition Classes
    GradientBoostedTreesModel → TreeEnsembleModel
    Annotations
    @Since( "1.2.0" )
  30. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Saveable

Inherited from TreeEnsembleModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped