ML0005 Discriminative and Generative

Discriminative Models:
Objective: Discriminative models are designed to draw a boundary between classes. They focus on modeling the conditional probability P(y∣x). They learn the mapping from features x to labels y without trying to model how the data was generated.
Examples: Logistic Regression, Support Vector Machines (SVMs), Neural Network Classifiers

Generative Models:
Generative Models: Estimate the joint probability P(x,y), or P(x∣y) and P(y), to understand how data is generated. Using Bayes’ theorem, they can then deduce the conditional probability P(y∣x) for classification tasks.
Examples: Naive Bayes, Hidden Markov Models (HMMs), Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).