# SplitCriterion

#### trait SplitCriterion extends Serializable

Trait for computing splitting criteria with respect to distributions of class values. The split criterion is used as a parameter on decision trees and decision stumps. The two split criteria most used are Information Gain and Gini.

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### Abstract Value Members

1. #### abstract def merit(pre: Array[Double], post: Array[Array[Double]]): Double

Computes the merit of splitting for a given distribution before and after the split.

Computes the merit of splitting for a given distribution before and after the split.

pre

the class distribution before the split

post

the class distribution after the split

returns

value of the merit of splitting

2. #### abstract def rangeMerit(pre: Array[Double]): Double

Computes the range of splitting merit

Computes the range of splitting merit

pre

the class distribution before the split

returns

value of the range of splitting merit

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