What is the difference between a generative and a discriminative algorithm?

Question

What is the difference between a generative and a discriminative algorithm?

Answer 1

Here's the most important part from the lecture notes of CS299 (by Andrew Ng) related to the topic, which really helps me understand the difference between discriminative and generative learning algorithms.

Suppose we have two classes of animals, elephant (y = 1) and dog (y = 0). And x is the feature vector of the animals.

Given a training set, an algorithm like logistic regression or the perceptron algorithm (basically) tries to find a straight line — that is, a decision boundary — that separates the elephants and dogs. Then, to classify a new animal as either an elephant or a dog, it checks on which side of the decision boundary it falls, and makes its prediction accordingly. We call these discriminative learning algorithm.

Here's a different approach. First, looking at elephants, we can build a model of what elephants look like. Then, looking at dogs, we can build a separate model of what dogs look like. Finally, to classify a new animal, we can match the new animal against the elephant model, and match it against the dog model, to see whether the new animal looks more like the elephants or more like the dogs we had seen in the training set. We call these generative learning algorithm.