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feature importance naive bayes python

neg_class_prob_sorted = NB_optimal.feature_log_prob_[0, :].argsort()[::-1]
pos_class_prob_sorted = NB_optimal.feature_log_prob_[1, :].argsort()[::-1]

print(np.take(count_vect.get_feature_names(), neg_class_prob_sorted[:10]))
print(np.take(count_vect.get_feature_names(), pos_class_prob_sorted[:10]))

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feature importance naive bayes python

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