org.apache.spark.mllib.tree.model

GradientBoostedTreesModel

class GradientBoostedTreesModel extends TreeEnsembleModel with Saveable

:: Experimental :: Represents a gradient boosted trees model.

Annotations
@Since( "1.2.0" ) @Experimental()
Linear Supertypes
Saveable, TreeEnsembleModel, Serializable, Serializable, AnyRef, Any
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  1. GradientBoostedTreesModel
  2. Saveable
  3. TreeEnsembleModel
  4. Serializable
  5. Serializable
  6. AnyRef
  7. Any
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Instance Constructors

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

    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: 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 ==(arg0: AnyRef): Boolean

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

    Definition Classes
    Any
  6. val algo: Algo

    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" )
  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  8. def clone(): AnyRef

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

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

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

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

    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" )
  13. def finalize(): Unit

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

    Current version of model save/load format.

    Current version of model save/load format.

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

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

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

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

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

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

    Definition Classes
    AnyRef
  21. def numTrees: Int

    Get number of trees in ensemble.

    Get number of trees in ensemble.

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

    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
  23. def predict(features: RDD[Vector]): RDD[Double]

    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
  24. def predict(features: Vector): Double

    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
  25. def save(sc: SparkContext, path: String): Unit

    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" )
  26. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  27. def toDebugString: String

    Print the full model to a string.

    Print the full model to a string.

    Definition Classes
    TreeEnsembleModel
  28. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

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

    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
  30. val treeWeights: Array[Double]

    tree ensemble weights

    tree ensemble weights

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

    tree ensembles

    tree ensembles

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

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

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Saveable

Inherited from TreeEnsembleModel

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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