How to get most informative features for scikit-learn classifiers?
classification, machine-learning, python, scikit-learn
Solution
With the help of larsmans code I came up with this code for the binary case:
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)
Problem
The classifiers in machine learning packages like liblinear and nltk offer a method `show_most_informative_features()`, which is really helpful for debugging features: ``` viagra = None ok : spam = 4.5 : 1.0 hello = True ok : spam = 4.5 : 1.0 hello = None spam : ok = 3.3 : 1.0 viagra = 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?