asking the model about the features using wrappers while filters can tremendously help in getting rid of useless featuresthey can go only so far after all the filteringthere might still be some features that are independent among themselves and show some degree of dependence with the result variablebut yet they are totally useless from the model' point of view just think of the following datawhich describes the xor function individuallyneither nor would show any signs of dependence on ywhereas together they clearly doa so why not ask the model itself to give its vote on the individual featuresthis is what wrappers doas we can see in the following process chart diagramy current featuresinitialized with all features xn train model with and check the importance of individual features importance of individual features feature set too big no resulting features yes drop features that are unimportant here we have pushed the calculation of feature importance to the model training process unfortunately (but understandably)feature importance is not determined as binary but as ranking value so we still have to specify where to make the cut what part of the features are we willing to take and what part do we want to drop