title: Biasing Sklearn toward positives For MultinomialNB
body: <p>I am trying to run multinomial naive bayes on a series of examples in python using sci kit learn. I am consitently getting all examples classified as negative. The training set is somewhat biased towards negatives P(negative) ~.75. I looked through the <a href="http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html" rel="nofollow">documentation</a> and I couldn't find a way to bias toward positives. </p>
  
  <pre><code>from sklearn.datasets import load_svmlight_file
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import recall_score
  from sklearn.metrics import precision_score
  X_train, y_train= load_svmlight_file("POS.train")
  x_test, y_test = load_svmlight_file("POS.val")
  clf = MultinomialNB()
  clf.fit(X_train, y_train)
  preds = clf.predict(x_test)
  print('accuracy: ' + str(accuracy_score(y_test, preds)))
  print('precision: ' + str(precision_score(y_test, preds)))
  print('recall: ' + str(recall_score(y_test, preds)))
  </code></pre>