# Regression coefficients vs feature_importances_ vs none

On looking at various machine learning methods at the scikit-learn site http://scikit-learn.org/stable/modules/classes.html , it appears that some modules such as linear regression ( http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html ) provide coefficients (coef_), others such as AdaBoostRegressor ( http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html ) provide feature_importances_ while some e.g. BaggingRegressor ( http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html ) do not provide either of these.

Why this difference? Are coefficients similar to feature_importances_ to assess the contribution of a variable in prediction? How to assess the feature importance for modules where neither of these is available e.g. in BaggingRegressor (link above) and BernoulliNB ( http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html )?