Gaussian Discriminant Analysis an example of Generative Learning Algorithms

Algorithms that model p(y|x) directly from the training set are called discriminative algorithms. There can be a different approach to the same problem, consider the same binary classification problem where we want learn to distinguish between two classes, class A (y=1) and class B (y=0) based on some features. Now we take all the examples of label A and try to learn the features and build a model for class A. Then we take all the examples labeled B and try to learn it’s features and build a separate model for class B. Finally to classify a new element, we match it against each model and see which one fits better (generate high value for probability). In this approach we try to model p(x|y) and p(y) as oppose to p(y|x) we did earlier, it’s called Generative Learning Algorithms. Once we learn the model p(y) and p(x|y) using training set, we use Bayes Rule to derive the p(y|x) as <a href="https://towardsdatascience.com/gaussian-discriminant-analysis-an-example-of-generative-learning-algorithms-2e336ba7aa5c">Website</a>