# Feature importance ratio

I trained a Random Forest classifier (sklearn) and consequently computed the feature importance and consequently ranked them. The forest has 100 estimators. My top 5 features with their importances are as here:

f1 = 0.91
f2 = 0.04
f3 = 0.013
f4 = 0.007
f5 = 0.004


To avoid over-fitting, I did the evaluation using cross-validation and learning curve.

My question is that the importance for f1 seems significantly higher than other features. Does it imply incorrectness (over-fitting?) of any sort? Should I do feature selection in some other way to generalize the model better?