A Popular Interview Question: Discriminative vs. Generative Models

The post explains the fundamental differences between discriminative and generative models in machine learning. Discriminative models focus on learning decision boundaries for classification by maximizing the conditional probability P(Y|X), with examples like logistic regression and decision trees. Generative models, such as Naive Bayes and Gaussian Mixture Models, learn the joint probability P(X, Y) and can generate new samples. A quiz is included to further illustrate the concepts and differentiate between the two approaches.