Partitioned Logistic Regression for Spam Filtering

Naive Bayes and logistic regression perform well in different regimes. While the former is a very simple generative model which is efficient to train and performs well empirically in many applications, the latter is a discriminative model which often achieves better accuracy and can be shown to outperform naive Bayes asymptotically. In this paper, the authors propose a novel hybrid model, partitioned logistic regression, which has several advantages over both naive Bayes and logistic regression.