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The goal of this function is to estimate the general performance of a classifier in a certain location. This perform will take as enter an m x (n+one) array of details, an integer p, the classifier it truly is examining, and any remaining parameters the classifier involves might be stored in args . Listed here’s how it works: It will eventually initially randomly combine-up the rows of knowledge. Then it will eventually partition knowledge into p equal areas. If p equivalent sections are not possible then as close to equivalent as is achievable (no mobile must have in excess of one more observation than one other cells).
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