I am using scikit-learn to build two binary classification models--one is a random forest, and the other is a linear SVM. I want to compare the relative importances of the input features to the trained models.
LinearSVC has an attribute "coef_" while the random forest classifier has an attribute "feature_importances_". I know that they are not computed on the same scale but, from what I've read, both represent the learned importances of the features.
If I transform the coefficients and feature importances to the same scale, would it be appropriate to compare the relative importances/weights assigned to each feature to understand how the two models differ in learning the data?