I have rather a simple question which I have not had any luck finding the answer to. I'm training a Random Forest classifier using sklearn in Python 2.7, on a large dataset ~(80k,250) where observations are movie reviews and features are attributes of critic and movie. Targets are "positive review" (1) or "negative review" (0).
My reason for training this classifier is so I can investigate feature importances. Just extracting the feature importances is a simple task, using
feature_importances_. This produces an array of GINI estimates for each feature. What this, however, does not tell me is which classification ("pos"/"neg") that a specific feature is typical to. In other words, does having Clint Eastwood in your movie affect the scores given by critics positively or negatively?
Is there a simple way to get these class-specific feature importances? Is there a complicated way? Is there a way at all?