org.apache.spark.mllib.tree.configuration

Strategy

class Strategy extends Serializable

:: Experimental :: Stores all the configuration options for tree construction

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@Experimental()
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Instance Constructors

  1. new Strategy(algo: Algo.Algo, impurity: Impurity, maxDepth: Int, maxBins: Int = 100, quantileCalculationStrategy: QuantileStrategy.QuantileStrategy = ..., categoricalFeaturesInfo: Map[Int, Int] = ..., maxMemoryInMB: Int = 128)

    algo

    classification or regression

    impurity

    criterion used for information gain calculation

    maxDepth

    maximum depth of the tree

    maxBins

    maximum number of bins used for splitting features

    quantileCalculationStrategy

    algorithm for calculating quantiles

    categoricalFeaturesInfo

    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.

    maxMemoryInMB

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

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.Algo

    classification or regression

  7. final def asInstanceOf[T0]: T0

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  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

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  10. final def eq(arg0: AnyRef): 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. val impurity: Impurity

    criterion used for information gain calculation

  16. final def isInstanceOf[T0]: Boolean

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  17. val maxBins: Int

    maximum number of bins used for splitting features

  18. val maxDepth: Int

    maximum depth of the tree

  19. val maxMemoryInMB: Int

    maximum memory in MB allocated to histogram aggregation.

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

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

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  21. final def notify(): Unit

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  22. final def notifyAll(): Unit

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  23. val quantileCalculationStrategy: QuantileStrategy.QuantileStrategy

    algorithm for calculating quantiles

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

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  25. def toString(): String

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  26. final def wait(): Unit

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

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

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