I am giving a toy example for describing a real world business problem. Let's say I am a publisher and I have some book stores to visit. By visiting those stores I will check whether they have sufficient stock of my books, they are visible on shelf etc. Now, I am training a model, to recommend me the stores to visit. I have, say, 20 features and some historical data which has a target variable which represents whether a store was visited or not (1/0). I trained a
RandomForestClassifier model on that data and this is the feature importance I got.
feature_14, feature_2 are more important than feature_11. Now, assume that feature_11 is a feature with high business importance. As an example, let's say feature_11 is the number of books on shelf and I want to put more importance on it in deciding whether to visit a store. Is there any way to put more importance on this feature_11 than feature_14 or feature_2? From historical data, model has learned that it is third most importance variable but I want to make it a key deciding factor. Is there any way of doing it?