DecisionTreeModel

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

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5. final def ==(arg0: Any): Boolean

   Definition Classes

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6. val algo: Algo

   algorithm type -- classification or regression

7. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

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   Annotations

   @throws( ... )

9. def depth: Int

   Get depth of tree.

   Get depth of tree. E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes.

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

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    Annotations

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

13. def formatVersion: String

    Current version of model save/load format.

    Current version of model save/load format.

    Attributes

    protected

    Definition Classes

    DecisionTreeModel → Saveable

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

    Definition Classes

    AnyRef → Any

15. def hashCode(): Int

    Definition Classes

    AnyRef → Any

16. final def isInstanceOf[T0]: Boolean

    Definition Classes

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

    Definition Classes

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18. final def notify(): Unit

    Definition Classes

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19. final def notifyAll(): Unit

    Definition Classes

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20. def numNodes: Int

    Get number of nodes in tree, including leaf nodes.

21. def predict(features: JavaRDD[Vector]): JavaRDD[Double]

    Predict values for the given data set using the model trained.

    Predict values for the given data set using the model trained.

    features

    JavaRDD representing data points to be predicted

    returns

    JavaRDD of predictions for each of the given data points

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

    Predict values for the given data set using the model trained.

    Predict values for the given data set using the model trained.

    features

    RDD representing data points to be predicted

    returns

    RDD of predictions for each of the given data points

23. 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

    Double prediction from the trained model

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

    Save this model to the given path.

    Save this model to the given path.

    This saves:

    • human-readable (JSON) model metadata to path/metadata/
    • Parquet formatted data to path/data/

    The model may be loaded using Loader.load.

    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

    DecisionTreeModel → Saveable

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

    Definition Classes

    AnyRef

26. def toDebugString: String

    Print the full model to a string.

27. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

    Definition Classes

    DecisionTreeModel → AnyRef → Any

28. val topNode: Node

    root node

29. final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )