You said something a bit self-contradictory at the end of your article: "I wouldn’t directly use feature importance measures for feature selection. We have enough methods to select non-performing features with feature selection. Feature importance like SHAP importance and others can, however, allow you to make qualitative decisions to remove a feature."
To me, the possibility to make qualitative decisions to remove a feature sounds very useful, and the suggestion that enough methods are available for feature selection seems somewhat random. So it would be very interesting to understand, why do you really not recommend using feature importance measures for feature selection. Is it because they are somewhat model-specific? Is it because many measures for feature importance are not reliable for strongly correlated features? Is there something else that I cannot think of right now?
You said something a bit self-contradictory at the end of your article: "I wouldn’t directly use feature importance measures for feature selection. We have enough methods to select non-performing features with feature selection. Feature importance like SHAP importance and others can, however, allow you to make qualitative decisions to remove a feature."
To me, the possibility to make qualitative decisions to remove a feature sounds very useful, and the suggestion that enough methods are available for feature selection seems somewhat random. So it would be very interesting to understand, why do you really not recommend using feature importance measures for feature selection. Is it because they are somewhat model-specific? Is it because many measures for feature importance are not reliable for strongly correlated features? Is there something else that I cannot think of right now?