Pros
- are fast to train and use for prediction. (Because naive simplifying assumption, only simple per class statistics need to be estimated for each feature and applied for each feature independently.)
- Works well with high-dimensional datasets. (SVM)
cons
- The assumption that features are conditionally independent given the class is not realistic.
- Generalization performance may worse than more sophisticated learning methods.