What is difference between generative and discriminative model?

Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model focuses on explaining how the data was generated, while a discriminative model focuses on predicting the labels of the data.

Are generative models slower than discriminative models?

3: Generative models often outperform discriminative models on smaller datasets because their generative assumptions place some structure on your model that prevent overfitting. For example, let’s consider Naive Bayes vs. Logistic Regression.

Is naive Bayes a generative or a discriminative model?

Naive bayes is a Generative model whereas Logistic Regression is a Discriminative model . Generative model is based on the joint probability, p( x, y), of the inputs x and the label y, and make their predictions by using Bayes rules to calculate p(y | x), and then picking the most likely label y.

What is a generative model explain how a discriminative model differentiates a real model from a fake model with an example?

A generative model could generate new photos of animals that look like real animals, while a discriminative model could tell a dog from a cat. Generative models capture the joint probability p(X, Y), or just p(X) if there are no labels. Discriminative models capture the conditional probability p(Y | X).

What’s the difference between discriminative and generative models?

The fundamental difference between discriminative models and generative models is: Discriminative models learn the (hard or soft) boundary between classes. Generative models model the distribution of individual classes.

Which is better generative or discriminative machine learning?

Generative models aim to capture the actual distribution of the classes in the dataset. Generative models predict the joint probability distribution – p (x,y) – utilizing Bayes Theorem. Generative models are computationally expensive compared to discriminative models. Generative models are useful for unsupervised machine learning tasks.

How is a SVM different from a generative model?

The fundamental difference between discriminative models and generative models is: SVMs and decision trees are discriminative because they learn explicit boundaries between classes. SVM is a maximal margin classifier, meaning that it learns a decision boundary that maximizes the distance between samples of the two classes, given a kernel.

What is the goal of a discriminative model?

With discriminative models, the goal is to identify the decision boundary between classes to apply reliable class labels to data instances. Discriminative models separate the classes in the dataset by using conditional probability, not making any assumptions about individual data points.