title: How is most informative feature percentage is calculated in Naive Bayes nltk python?
body: <p>We usually get the following results when we run the below command :-</p>
  
  <pre><code> classifier.show_most_informative_features(10)
  </code></pre>
  
  <p>Results:</p>
  
  <pre><code>Most Informative Features
               outstanding = 1                 pos : neg    =     13.9 : 1.0
                 insulting = 1                 neg : pos    =     13.7 : 1.0
                vulnerable = 1                 pos : neg    =     13.0 : 1.0
                 ludicrous = 1                 neg : pos    =     12.6 : 1.0
               uninvolving = 1                 neg : pos    =     12.3 : 1.0
                astounding = 1                 pos : neg    =     11.7 : 1.0
  </code></pre>
  
  <p>Does anybody knows how this 13.9, 13.7 etc is calculated? </p>
  
  <p>Also, We can get the most informative features with the following method classifier.show_most_informative_features(10) with Naive bayes but if we want to get the same results using logistic regression, please could somebody suggest the way to get that. I saw one post on stackoverflow but that requires vector which I am not using to create features.</p>
  
  <pre><code>classifier = nltk.NaiveBayesClassifier.train(train_set)
  print("Original Naive bayes accuracy percent: ", nltk.classify.accuracy(classifier,dev_set)* 100)
  classifier.show_most_informative_features(10)
  
  LogisticRegression_classifier = SklearnClassifier(LogisticRegression())
  LogisticRegression_classifier.train(train_set)
  print("LogisticRegression  accuracy percent: ", nltk.classify.accuracy(LogisticRegression_classifier, dev_set)*100)
  </code></pre>