Naive Bayes Classifiers – Python
From the post:
In naive Bayes classifiers, every feature gets a say in determining which label should be assigned to a given input value. To choose a label for an input value, the naive Bayes classifier begins by calculating the prior probability of each label, which is determined by checking the frequency of each label in the training set. The contribution from each feature is then combined with this prior probability, to arrive at a likelihood estimate for each label. The label whose likelihood estimate is the highest is then assigned to the input value.
Just one recent post from Python Language Processing. There are a number of others, some of which I will call out in future posts.