In many GBM models you can get a rough feature importance of a feature by taking the number of splits done on that feature and comparing it to the splits on the other features. This works rather well until you get a mix of categorical and continuous features.When you mix the continuous and categorical features the continuous features completely dominate as they have so many more places to split.Is there a known method on how to compare the feature importance of continuous and categorical features from this kind of measure? If not, what suggestions do you have on doing this?
Comparing Categorical and Continuous Features using Splits in GBM
feature selection;decision trees;xgboost;feature engineering;gbm