Discriminative models

• Model P(Y|X)

• Construct a single model for all the data

• Predict labels based on observed data

• Requires fewer data, fast, good performance even with small dataset

Generative models

• Model P(X,Y)

• Need to construct a model for each label (yi). During prediction, select yi based on the highest probability from each of the model of yi. So no boundary.

• Need a lot of data to build the model, but the information is complete. Can do more than predicting labels.