1. Naive Bayes
Gaussian Naive Bayes classifiers: high-dimensional data.
Bernoulli & Multinomial: text classification( sparse features vector, features are a large number of distinct words)
- Bernoulli: use a set of binary occurrence features(word presence/absence)
- Multinomial: use a set of count-based discrete features( word count)
Assumption
- Assume features are conditionally independent, given the class.
- In practice, of course, this is not often the case, features often are somewhat correlated. In practices, features are correlated.
1.1 Multinomial Naive Bayes classifiers