# Deep Learning in 5 minutes Part 3: Discriminative vs Generative Models

In machine learning field is common to talk about ** discriminative** and

**. Before to talk about autoencoders and GANs it is important to make clear some concepts.**

*generative models*#### Discriminative Models

A Discriminative model models the **decision boundary between the classes **and** **learns the **conditional probability distribution p(y|x) **[1]**. **Some examples of discriminative models are logistic regression, SVMs, ANN, KNN and Conditional Random Fields.

#### Generative Models

A Generative Model explicitly models the **actual distribution of each class **and** **learns the **joint probability distribution p(x,y).** It predicts the conditional probability with the help of **Bayes Theorem **[1]. Some examples of generative models are Näive Bayes, Gaussians, HMM, Mixture of Gaussians, Bayesian networks, Markov Random Fields and Mixture of multinomials.

#### Generative — Discriminative Pairs

While Näive Bayes and Logistic Regression are a pair for classification; HMM and CRF are a corresponding pair for sequential data.

#### To recap

Generative classifiers

- Assume some functional form for
**P(Y), P(X|Y)** - Estimate parameters of
**P(X|Y), P(Y)**directly from training data - Use Bayes rule to calculate
**P(Y |X)**

Discriminative Classifiers

- Assume some functional form for
**P(Y|X)** - Estimate parameters of
**P(Y|X)**directly from training data [1].

### Resources

- Generative vs Discriminative Models http://bit.ly/2Awl5dx
- Machine Learning: Generative and Discriminative Models — CEDAR http://bit.ly/2AwYQVc
- Generative and Discriminative Models http://bit.ly/2AsPtWh