# Biasing SkLearn Algorithms to Positive Outcomes

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 ratio of positives to negatives in my training set is ~1:3) Is there a way in sklearn to bias toward positive examples? I have coded it as follows:

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
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)))


Check out the preprocessing module in sklearn link
Alternatively, you could try setting the class_prior argument.