document: 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 , we propose a novel hybrid model , partitioned logistic regression , which has several advantages over both naive Bayes and logistic regression . This model separates the original feature space into several disjoint feature groups . Individual models on these groups of features are learned using logistic regression and their predictions are combined using the naive Bayes principle to produce a robust final estimation . We show that our model is better both theoretically and empirically . In addition , when applying it in a practical application , email spam filtering , it improves the normalized AUC score at 10 % false-positive rate by 28.8 % and 23.6 % compared to naive Bayes and logistic regression , when using the exact same training examples .
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