How to get most informative features for scikit-learn classifiers?

Question

The classifiers in machine learning packages like liblinear and nltk offer a method show_most_informative_features(), which is really helpful for debugging features: ****** = None ok : spam = 4.5 : 1.0 hello = True ok : spam = 4.5 : 1.0 hello = None spam : ok = 3.3 : 1.0 ****** = True spam : ok = 3.3 : 1.0 casino = True spam : ok = 2.0 : 1.0 casino = None ok : spam = 1.5 : 1.0 My question is if something similar is implemented for the classifiers in scikit-learn. I searched the documentation, but couldn't find anything the like. If there is no such function yet, does somebody know a workaround how to get to those values? Thanks a lot! Select the correct answer from above options

Answer 1

You can extract your features using a Vectorizer/CountVectorizer/TfidfVectorizer/DictVectorizer, and you are using a linear model. You can use this code for binary classification: def show_most_informative_features(vectorizer, clf, n=20): feature_names = vectorizer.get_feature_names() coefs_with_fns = sorted(zip(clf.coef_[0], feature_names)) top = zip(coefs_with_fns[:n], coefs_with_fns[:-(n +1):-1]) for (coef_1, fn_1), (coef_2, fn_2) in top: print("\t%.4f\t%-15s\t\t%.4f\t%-15s" %(coef_1, fn_1, coef_2, fn_2)) Hope this answer helps.