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io.citrine.lolo.trees.splits

RegressionSplitter

object RegressionSplitter

Find the best split for regression problems.

The best split is the one that reduces the total weighted variance: totalVariance = N_left * \sigma_left2 + N_right * \sigma_right2 which, in scala-ish, would be: totalVariance = leftWeight * (leftSquareSum /leftWeight - (leftSum / leftWeight )2) + rightWeight * (rightSquareSum/rightWeight - (rightSum / rightWeight)2) Because we are comparing them, we can subtract off leftSquareSum + rightSquareSum, which yields the following simple expression after some simplification: totalVariance = -leftSum * leftSum / leftWeight - Math.pow(totalSum - leftSum, 2) / (totalWeight - leftWeight) which depends only on updates to leftSum and leftWeight (since totalSum and totalWeight are constant).

Created by maxhutch on 11/29/16.

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  9. def getBestCategoricalSplit(data: Seq[(Vector[AnyVal], Double, Double)], calculator: VarianceCalculator, index: Int, minCount: Int): (CategoricalSplit, 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

  10. def getBestRealSplit(data: Seq[(Vector[AnyVal], Double, Double)], calculator: VarianceCalculator, 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

    returns

    the best split of this feature

  11. def getBestSplit(data: Seq[(Vector[AnyVal], Double, 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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