CODEWITHSUNDEEP
pythontextclassificationnltk
English Grad
English Grad

Reputation: 1395

python nltk naive bayes probabilities

Is there a way to get at the individual probabilities using nltk.NaiveBayesClassifier.classify? I want to see the probabilities of classification to try and make a confidence scale. Obviously with a binary classifier the decision is going to be one or the other, but is there some way to see the inner workings of how the decision was made? Or, do I just have to write my own classifier?

Thanks

Upvotes: 11

Views: 6341

Answers (2)

Luciano Camilo
Luciano Camilo

Reputation: 99

I know this is utterly old. But as I struggled some time to found this out i sharing this code.

It show the probability associatte with each feature in Naive Bayes Classifer. It helps me understand better how show_most_informative_features worked. Possible it is the best option to everyone (and much possible that's why they created this funcion). Anyway, for those like me that MUST SEE the individual probabily for each label and word, you can use this code:

for label in classifier.labels():
  print(f'\n\n{label}:')
  for (fname, fval) in classifier.most_informative_features(50):
    print(f"   {fname}({fval}): ", end="")
    print("{0:.2f}%".format(100*classifier._feature_probdist[label, fname].prob(fval)))

Upvotes: 0

ales_t
ales_t

Reputation: 2017

How about nltk.NaiveBayesClassifier.prob_classify?

http://nltk.org/api/nltk.classify.html#nltk.classify.naivebayes.NaiveBayesClassifier.prob_classify

classify calls this function:

def classify(self, featureset):
    return self.prob_classify(featureset).max()

Edit: something like this should work (not tested):

dist = classifier.prob_classify(features)
for label in dist.samples():
    print("%s: %f" % (label, dist.prob(label)))

Upvotes: 20

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