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object Splitter

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  1. final def !=(arg0: Any): Boolean
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  9. def getBestRealSplit[T](data: Seq[(Vector[AnyVal], T, Double)], calculator: ImpurityCalculator[T], index: Int, minCount: Int, randomizePivotLocation: Boolean = false, rng: Random = Random): (Split, Double)

    Find the best split on a continuous variable.

    Find the best split on a continuous variable. This is a typical implementation for decision trees: it finds the pivot that maximizes the decrease in impurity. Some splitters might forego this method and implement different ways of choosing a pivot point.

    data

    to split

    index

    of the feature to split on

    minCount

    minimum number of data points to allow in each of the resulting splits

    randomizePivotLocation

    whether generate splits by drawing a random value uniformly between the two split points. This can improve generalizability, particularly as part of an ensemble.

    returns

    the best split of this feature

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