GradientBoostedTreesModel

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

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 finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

13. final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

14. def hashCode(): Int

    Definition Classes

    AnyRef → Any

15. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

16. final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

17. final def notify(): Unit

    Definition Classes

    AnyRef

18. final def notifyAll(): Unit

    Definition Classes

    AnyRef

19. def numTrees: Int

    Get number of trees in forest.

    Get number of trees in forest.

    Definition Classes

    TreeEnsembleModel

20. 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

21. 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

22. 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

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

    Definition Classes

    AnyRef

24. def toDebugString: String

    Print the full model to a string.

    Print the full model to a string.

    Definition Classes

    TreeEnsembleModel

25. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

    Definition Classes

    TreeEnsembleModel → AnyRef → Any

26. def totalNumNodes: Int

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

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

    Definition Classes

    TreeEnsembleModel

27. val treeWeights: Array[Double]

    tree ensemble weights

    tree ensemble weights

    Definition Classes

    GradientBoostedTreesModel → TreeEnsembleModel

28. val trees: Array[DecisionTreeModel]

    tree ensembles

    tree ensembles

    Definition Classes

    GradientBoostedTreesModel → TreeEnsembleModel

29. final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

30. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

31. final def wait(arg0: Long): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )