**How it works (in a nutshell)**

A family of probabilistic classifiers in which the features are assumed to be conditionally-independent (their joint distribution equals the product of their marginals). What's gaussian about it? GNB uses gaussian distributions to model features with continuous data.

**Applications**

- Spam filtering
- Face recognition
- Document classification (i.e. machine learning research, poetry, anthropology)

**Perks**

- Can perform well in small datasets
- Can be used when we know the relative ordering of the probabilities in question, but perhaps not the probabilities themselves
- Simple complexity O(classes X features)

**Drawbacks**

- The conditionally independent assumption may mask patterns associated with co-varying features.

**Resources**

On the Optimality of the Simple Bayesian Classifier under Zero-One Loss