object BoltzmannSplitter extends Serializable
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- case class SplitterResult(split: Split, variance: Double, rebasedScore: Double, base: Double) extends Product with Serializable
Container for function returns, like a decorated tuple
Container for function returns, like a decorated tuple
The true score (proportional to draw probability) is rebasedScore * Math.exp(base). This decomposition is such that rebasedScore should always be >= 1.0.
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- def getBestCategoricalSplit(data: Seq[(Vector[AnyVal], Double, Double)], calculator: VarianceCalculator, index: Int, minCount: Int, beta: Double, rng: Random): Option[SplitterResult]
Find the best categorical splitter.
Find the best categorical splitter.
- data
to split
- calculator
that will efficiently compute the impurity (variance in this case)
- index
of the feature to split on
- minCount
minimum number of training instances to leave in each of the children nodes
- beta
the inverse temperature (1.0 / (temperature * initial variance)) to scale the variances by
- returns
the best split of this feature, along with its score, base, and result variance
- def getBestRealSplit(data: Seq[(Vector[AnyVal], Double, Double)], calculator: VarianceCalculator, index: Int, minCount: Int, beta: Double, rng: Random): Option[SplitterResult]
Find the best split on a continuous variable
Find the best split on a continuous variable
- data
to split
- calculator
that will efficiently compute the impurity (variance in this case)
- index
of the feature to split on
- minCount
minimum number of training instances to leave in each of the children nodes
- beta
the inverse temperature (1.0 / (temperature * initial variance)) to scale the variances by
- returns
the best split of this feature, along with its score, base, and result variance
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