Strategy

Value Members

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

   Definition Classes

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

   Definition Classes

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3. final def ##(): Int

   Definition Classes

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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.Algo

   Learning goal.

   Learning goal. Supported: org.apache.spark.mllib.tree.configuration.Algo.Classification, org.apache.spark.mllib.tree.configuration.Algo.Regression

7. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

8. val categoricalFeaturesInfo: Map[Int, Int]

   A map storing information about the categorical variables and the number of discrete values they take.

   A map storing information about the categorical variables and the number of discrete values they take. For example, an entry (n -> k) implies the feature n is categorical with k categories 0, 1, 2, ... , k-1. It's important to note that features are zero-indexed.

9. def clone(): AnyRef

   Attributes

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10. final def eq(arg0: AnyRef): Boolean

    Definition Classes

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11. def equals(arg0: Any): Boolean

    Definition Classes

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12. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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

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

    Definition Classes

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14. def hashCode(): Int

    Definition Classes

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15. val impurity: Impurity

    Criterion used for information gain calculation.

    Criterion used for information gain calculation. Supported for Classification: org.apache.spark.mllib.tree.impurity.Gini, org.apache.spark.mllib.tree.impurity.Entropy. Supported for Regression: org.apache.spark.mllib.tree.impurity.Variance.

16. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

17. val isMulticlassClassification: Boolean

18. val isMulticlassWithCategoricalFeatures: Boolean

19. val maxBins: Int

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity.

20. val maxDepth: Int

    Maximum depth of the tree.

    Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes.

21. val maxMemoryInMB: Int

    Maximum memory in MB allocated to histogram aggregation.

    Maximum memory in MB allocated to histogram aggregation. Default value is 128 MB.

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

    Definition Classes

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23. final def notify(): Unit

    Definition Classes

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24. final def notifyAll(): Unit

    Definition Classes

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25. val numClassesForClassification: Int

    Number of classes for classification.

    Number of classes for classification. (Ignored for regression.) Default value is 2 (binary classification).

26. val quantileCalculationStrategy: QuantileStrategy.QuantileStrategy

    Algorithm for calculating quantiles.

    Algorithm for calculating quantiles. Supported: org.apache.spark.mllib.tree.configuration.QuantileStrategy.Sort

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

    Definition Classes

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28. def toString(): String

    Definition Classes

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

    Definition Classes

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    @throws( ... )