The Generative model is considered as a class of statistical models that can generate new data instances. This model is typically used to estimate probabilities, modeling data points and distinguishing between classes based on these probabilities. For eg- Naive Bayes or Bayesian networks, Gaussian Mixture Model (GMM), Hidden Markov model, Linear Discriminant Analysis (LDA).

Whereas Discriminative model refers to a class of models used in statistical classification, especially in supervised machine learning. Also known as conditional models, generative modeling learns the boundary between classes or labels in a dataset. It tends to model the joint probability of data points and can create new instances using probability estimates and maximum likelihood. For eg- Logistic regression, Support vector machine, Decision tree, Random forests.

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Generative models try to model how data is placed throughout the space, while discriminative models attempt to draw boundaries in the data space. Generative modeling contrasts with discriminative modeling, which recognizes existing data and can be used to classify data. Generative modeling produces something and discriminative modeling identifies tags and sorts data.

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Machine Learning Models: Generative vs. Discriminative
Generative and discriminative machine learning models employ different approaches to solve the problem of categorizing objects. As generative models generate new data instances, discriminative models distinguish between various kinds of data instances.
Source- https://www.analyticsinsight.net/machine-learning-models-generative-vs-discriminative/

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