A functionality that usually takes as enter the output from (2) and outputs a boolean indicating if the process percolates or not.
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The goal of this operate should be to estimate the functionality of the classifier in a specific placing. This function can take as input an m x (n+1) variety of details, an integer p, the classifier it is examining, and any remaining parameters which the classifier calls for will be saved in args . Here’s how it really works: It can 1st randomly combine-up the rows of knowledge. Then it can partition facts into p equivalent sections. If p equivalent areas are impossible then as near equivalent as is achievable (no cell should have much more than 1 a lot more observation than another cells).
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