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Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of unobserved (target) variables on observed variables . Within a probabilistic framework, this is done by modeling the conditional probability distribution , which can be used for predicting from .

Discriminative models, as opposed to generative models, do not allow one to generate samples from the joint distribution of observed and target variables. However, for tasks such as classification and regression that do not require the joint distribution, discriminative models can yield superior performance (in part because they have fewer variables to compute).[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 support unsupervised learning. Application-specific details ultimately dictate the suitability of selecting a discriminative versus generative model.


Examples of discriminative models used in machine learning include:

See alsoEdit


  1. ^ Singla, Parag; Domingos, Pedro (2005). "Discriminative Training of Markov Logic Networks". Proceedings of the 20th National Conference on Artificial Intelligence - Volume 2. AAAI'05. Pittsburgh, Pennsylvania: AAAI Press: 868–873. ISBN 157735236X. 
  2. ^ J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In ICML, 2001.
  3. ^ Ng, Andrew Y.; Jordan, Michael I. (2001). On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes.