sklearn: get feature names after L1-based feature selection

Question

This question and answer demonstrate that when feature selection is performed using one of scikit-learn\'s dedicated feature selection routines, then the names of the selected f

Answer 1

For sparse estimators you can generally find the support by checking where the non-zero entries are in the coefficients vector (provided the coefficients vector exists, which is the case for e.g. linear models)

support = np.flatnonzero(estimator.coef_)

For your LinearSVC with l1 penalty it would accordingly be

from sklearn.svm import LinearSVC
svc = LinearSVC(C=1., penalty='l1', dual=False)
svc.fit(X, y)
selected_feature_names = np.asarray(vectorizer.get_feature_names())[np.flatnonzero(svc.coef_)]