Discriminative model

Discriminative models are a class of models used in machine learning for modeling the dependence of an unobserved variable y on an observed variable x. Within a statistical framework, this is done by modeling the conditional probability distribution P(y | x), which can be used for predicting y from x.

Discriminative models differ from generative models in that they do not allow one to generate samples from the joint distribution of x and y. However, for tasks such as classification and regression that do not require the joint distribution, discriminative models generally yield superior performance. On the other hand, generative models are typically more flexible than discriminative models in expressing dependencies in complex learning tasks. In addition, most discriminative models are inherently supervised and cannot easily be extended to unsupervised learning.

Examples of discriminative models used in machine learning include:

• Logistic regression, a type of generalized linear regression used for predicting binary or categorical outputs (also known as maximum entropy classifiers)
• Linear discriminant analysis
• Support vector machines
• Boosting
• Conditional random fields
• Linear regression
• Neural networks

See also

• Generative model