Discriminative model

Discriminative models, also called conditional 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 probabilistic framework, this is done by modeling the conditional probability distribution $P(y|x)$, which can be used for predicting $y$ from $x$.

Discriminative models, as opposed to generative models, 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 can yield superior performance.^[1][2][3] 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. Application specific details ultimately dictate the suitability of selecting a discriminative versus generative model.

Examples

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)
• Support vector machines
• Boosting (meta-algorithm)
• Conditional random fields
• Linear regression
• Neural networks

See also

• Generative model

References

1. P. Singla and P. Domingos. Discriminative training of Markov logic networks. In AAAI, 2005.
2. J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In ICML, 2001.
3. A. Ng and M. I. Jordan. On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes. In NIPS, 2001