case class ExtraRandomClassificationSplitter(rng: Random = Random) extends Splitter[Char] with Product with Serializable
A splitter that defines Extremely Randomized Trees
This is based on Geurts, P., Ernst, D. & Wehenkel, L. Extremely randomized trees. Mach Learn 63, 3–42 (2006). https://doi.org/10.1007/s10994-006-6226-1.
- rng
random number generator
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- ExtraRandomClassificationSplitter
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- new ExtraRandomClassificationSplitter(rng: Random = Random)
- rng
random number generator
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- def getBestCategoricalSplit(data: Seq[(Vector[AnyVal], Char, Double)], calculator: ImpurityCalculator[Char], index: Int, minCount: Int): (Split, Double)
Get find the best categorical splitter.
Get find the best categorical splitter.
- data
to split
- index
of the feature to split on
- returns
the best split of this feature
- def getBestRealSplit(data: Seq[(Vector[AnyVal], Char, Double)], calculator: ImpurityCalculator[Char], index: Int, minCount: Int): (RealSplit, Double)
Find the best split on a continuous variable.
Find the best split on a continuous variable.
- data
to split
- index
of the feature to split on
- minCount
minimum number of data points to allow in each of the resulting splits
- returns
the best split of this feature
- def getBestSplit(data: Seq[(Vector[AnyVal], Char, Double)], numFeatures: Int, minInstances: Int): (Split, Double)
Get the best split, considering numFeature random features (w/o replacement)
Get the best split, considering numFeature random features (w/o replacement)
- data
to split
- numFeatures
to consider, randomly
- returns
a split object that optimally divides data
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- ExtraRandomClassificationSplitter → Splitter
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- def productElementNames: Iterator[String]
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- val rng: Random
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