feature importance naive bayes python

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1neg_class_prob_sorted = NB_optimal.feature_log_prob_[0, :].argsort()[::-1]
2pos_class_prob_sorted = NB_optimal.feature_log_prob_[1, :].argsort()[::-1]
3
4print(np.take(count_vect.get_feature_names(), neg_class_prob_sorted[:10]))
5print(np.take(count_vect.get_feature_names(), pos_class_prob_sorted[:10]))
6

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