org.apache.spark.mllib.tree.impurity

Impurity

trait Impurity extends Serializable

:: Experimental :: Trait for calculating information gain. This trait is used for (a) setting the impurity parameter in org.apache.spark.mllib.tree.configuration.Strategy (b) calculating impurity values from sufficient statistics.

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@Since( "1.0.0" ) @Experimental()
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Entropy, Gini, Variance
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  1. abstract def calculate(count: Double, sum: Double, sumSquares: Double): Double

    :: DeveloperApi :: information calculation for regression

    :: DeveloperApi :: information calculation for regression

    count

    number of instances

    sum

    sum of labels

    sumSquares

    summation of squares of the labels

    returns

    information value, or 0 if count = 0

    Annotations
    @Since( "1.0.0" ) @DeveloperApi()

  2. abstract def calculate(counts: Array[Double], totalCount: Double): Double

    :: DeveloperApi :: information calculation for multiclass classification

    :: DeveloperApi :: information calculation for multiclass classification

    counts

    Array[Double] with counts for each label

    totalCount

    sum of counts for all labels

    returns

    information value, or 0 if totalCount = 0

    Annotations
    @Since( "1.1.0" ) @DeveloperApi()

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