Generative vs Discriminative

A generative model will learn categories of data while a discriminative model will simply learn the distinction between different categories of data.

Generative models learn the joint probability distribution p(x, y) and can generate new data points, while discriminative models learn the conditional probability p(x|y) and are used for predicting the output given an input.

Generative models include algorithms like Naive Bayes and GANs, capturing data distributions

Discriminative models, such as logistic regression and neural networks, focus on separating different classes. Discriminative models will generally outperform generative models on classification tasks.

Discriminative model learns the predictive distribution p(y|x) directly while generative model learns the joint distribution p(x, y) then obtains the predictive distribution based on Bayes' rule.

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

Let's say you have input data x and you want to classify the data into labels y. A generative model learns the joint probability distribution p(x,y) and a discriminative model learns the conditional probability distribution p(y|x) - which you should read as "the probability of y given x".

https://stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-a-discriminative-algorithm