I have tried using Naive bayes on a labeled data set of crime data but got really poor results (7% accuracy). Naive Bayes runs much faster than other alogorithms I've been using so I wanted to try finding out why the score was so low.
After reading I found that Naive bayes should be used with balanced datasets because it has a bias for classes with higher frequency. Since my data is unbalanced I wanted to try using the Complementary Naive Bayes since it is specifically made for dealing with data skews. In the paper that describes the process, the application is for text classification but I don't see why the technique wouldn't work in other situations. You can find the paper I'm referring to here. In short the idea is to use weights based on the occurences where a class doesn't show up.
After doing some research I was able to find an implementation in Java but unfortunately I don't know any Java and I just don't understand the algorithm well enough to implement myself.
where I can find an implementation in python? If that doesn't exist how should I go about implementing it myself?