3.8. Naive Bayes¶
Naive Bayes algorithms are a set of supervised learning methods based on applying Baye’s theorem with strong (naive) independence assumptions.
The advantage of Naive Bayes approaches are:
- It requires a small amount of training data to estimate the parameters necessary for classification.
- In spite of their naive design and apparently over-simplified assumptions, naive Bayes classifiers have worked quite well in many complex real-world situations.
- The decoupling of the class conditional feature distributions means that each distribution can be independently estimated as a one dimensional distribution. This in turn helps to alleviate problems stemming from the curse of dimensionality.