Regression coefficients vs feature_importances_ vs none On looking at various machine learning methods at the scikit-learn, it appears that some modules such as LinearRegression provide coefficients (coef_), others such as AdaBoostRegressor provide feature_importances_ while some e.g. BaggingRegressor and BernoulliNB do not provide either of these.
Why this difference? Are coef_ 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?
The main difference between Linear Regression and Tree-based methods is that Linear Regression is parametric: it can be writen with a mathematical closed expression depending on some parameters. Therefore, the coefficients are the parameters of the model, and should not be taken as any kind of importances unless the data is normalized. 

On the other hand, non-parametric methods have very different ways of measure the importance. In layman terms, measuring the importance of variables on a tree can be done by checking how close they appear to the root node. In ensembling methods, like bagging, one can compute the importance of a variable as the average among the ensemble, like in this stackoverflow answer.

The main difference is, then, the fact that parametric models have, through their parameters, a way of showing the importance of the variables, while non parametric models need some extra work.