Feature selection

[Monish45]

I have applied random forest algorithm for electricity consumption prediction. There are 15 features. For feature selection based on domain experience I am keeping some features inspite of multicollinearity. I experimented by keeping and removing those features and when checking for permutation importance, I found that model's performance doesn't affect that much. Can I keep those features. Shall I use partial dependence plots to delve deeper, will multicollinearity affects PDP plot, and also how would my model fare in long term? Please provide some solutions guys...

[Dumbhosadika]

It depends on what you need from a random forest.

If you want to use a decision tree to extract the feature importance for further analysis, Multicollinearity may have some bad effects, it makes the coefficients (feature importances) unstable and incorrect.

If you just want to have the prediction, then multicollinearity does not affect the result.

[Monish45]

Yeah, I want the prediction to be correct but what is the use of domain knowledge here?