Permutation feature importance (PFI) is a nice way of getting feature importance in black-box models or models where it is difficult to characterise the relationship between the features and the response. However, it suffers from when the features are highly correlated, which can lead to weird results.
Anyone has experienced with this technique on a correlated dataset? Two solutions I am thinking about are:
- Remove the highly colinear altogether.
- Group the highly correlated together and shuffle their values at the same time (rather than doing it one column at a time) to get a single importance score for that group of correlated features.
Anyone has other propositions?