splitting criteria
Splitting criteria refers to a rule or condition used in decision tree algorithms to determine how to divide a larger dataset into smaller, more specific subsets based on the values of certain input variables. The goal of splitting criteria is to create subgroups that are more homogeneous in terms of their predicted outcomes, ultimately leading to a more accurate model.
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