object Splitter
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- 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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