Statistical checks may be used to pick those features which have the strongest partnership Using the output variable.
The instance underneath works by using the chi squared (chi^two) statistical check for non-adverse characteristics to pick four of the greatest attributes from the Pima Indians onset of diabetes dataset.
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But i also want to check product performnce with diverse team of characteristics one by one so do i must do gridserach over and over for each function group?
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I am new to ML and am doing a project in Python, at some point it can be to acknowledge correlated attributes , I ponder what would be the next step?
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…is that you may get the exact same number more than after. So how can we Be sure that we get 10 diverse figures?
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