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The goal of this perform would be to estimate the overall performance of the classifier in a selected setting. This perform will take as input an m x (n+1) array of knowledge, an integer p, the classifier it truly is checking, and any remaining parameters that the classifier calls for will probably be stored in args . In this article’s how it works: It is going to first randomly blend-up the rows of data. Then it will eventually partition knowledge into p equivalent elements. If p equivalent sections are not possible then as near to equal as is achievable (no cell must have official site over one a lot more observation than the other cells).
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I got your position and at the same time, in general, I don't believe in "getting just one solution". My issue with invoking an item with -m is twofold.
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