hencewe have to calculate the normalized mutual information for all feature pairs for every pair having very high value (we would have to determine what that means)we would then drop one of them in case we are doing regressionwe could drop the feature that has very low mutual information with the desired result value this might work for small set of features at some pointhoweverthis procedure can be really expensiveas the amount of calculation grows quadratically since we are computing the mutual information between feature pairs another huge disadvantage of filters is that they drop features that are not useful in isolation more often than notthere are handful of features that seem to be totally independent of the target variableyet when combined togetherthey rock to keep thesewe need wrappers