n_ features_ to_select support_ ranking_ [false true false true false false false false true true[ [false true true true false false false false true true[ true true true true false false false false true true[ true true true true false true false false true true[ true true true true false true false true true true[ true true true true false true true true true true[ true true true true true true true true true true[ we see that the result is very stable features that have been used when requesting smaller feature sets keep on getting selected when letting more features in finally we rely on our train/test set splitting to warn us when we go in the wrong direction other feature selection methods there are several other feature selection methods that you will discover while reading through machine learning literature some don' even look like feature selection methods as they are embedded into the learning process (not to be confused with the previously mentioned wrappersdecision treesfor instancehave feature selection mechanism implanted deep in their core other learning methods employ some kind of regularization that punishes model complexitythus driving the learning process towards good performing models that are still "simplethey do this by decreasing the less impactful featuresimportance to zero and then dropping them ( -regularizationso watch outoftenthe power of machine learning methods has to be attributed to their implanted feature selection method to great degree