Naive Bayes classifiers are a family of probabilistic classifiers on applying the Bayes' conditional probability theorem. These classifiers assume independence between the features. Naive Bayes is often the baseline method for text categorization with word frequencies as the set. Despite the strong independence assumptions, the Naive Bayes classifiers are fast and easy to implement; hence, they are used very commonly in practice.
While Naive Bayes is very popular, it also suffers from errors that can lead to favoring of one class over the other(s). For example, skewed data can cause the classifier to favor one class over another. Similarly, the independence assumption can lead to erroneous classification weights that one class over another.
Note
For specific heuristics for dealing with problems associated with Naive Bayes classifers, refer to Tackling the Poor Assumptions of Naive Bayes Text Classifiers, by Rennie, Shih, et al at https://people.csail.mit.edu...
