Naive Bayes Continuous is a supervised learning component. It implements the naive bayes principle for continuous predictors (gaussian assumption, heteroscedasticity or homoscedasticity). The main originality is that it provides an explicit model corresponding to a linear combination of predictors and, eventually, their square.
Enhancement of the reporting module.
