Naive Bayes classifier: True and estimated errors for 2-class, 2-features case
Zoë Hoare · 2006 3rd International IEEE Conference Intelligent Systems · 2006
The low error rate of naive Bayes (NB) classifier has been described as surprising. It is known that class conditional independence of the features is sufficient but not a necessary condition for optimality of NB. This study is about the difference between the estimated error and the true error of NB taking into account feature dependencies. Analytical results are derived for two binary features. Illustration examples are also provided