org.apache.spark.mllib.tree.model

DecisionTreeModel

class DecisionTreeModel extends Serializable

:: Experimental :: Decision tree model for classification or regression. This model stores the decision tree structure and parameters.

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@Experimental()
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Instance Constructors

  1. new DecisionTreeModel(topNode: Node, algo: Algo)

    topNode

    root node

    algo

    algorithm type -- classification or regression

Value Members

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

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

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  3. final def ##(): Int

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  4. final def ==(arg0: AnyRef): Boolean

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

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  6. val algo: Algo

    algorithm type -- classification or regression

  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @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

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def numNodes: Int

    Get number of nodes in tree, including leaf nodes.

  20. 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

  21. 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

  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

    Double prediction from the trained model

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

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  24. def toDebugString: String

    Print the full model to a string.

  25. def toString(): String

    Print a summary of the model.

    Print a summary of the model.

    Definition Classes
    DecisionTreeModel → AnyRef → Any

  26. val topNode: Node

    root node

  27. final def wait(): Unit

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

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

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