Probabilistic classification: Difference between revisions

Revision as of 14:46, 15 July 2019

Revision as of 15:50, 15 August 2019

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==Generative and conditional training==

Some models, such as [[logistic regression]], are conditionally trained: they optimize the conditional probability <math>\Pr(Y \vert X)</math> directly on a training set (see [[empirical risk minimization]]). Other classifiers, such as [[naive Bayes]], are trained [[Generative model|generatively]]: at training time, the class-conditional distribution <math>\Pr(X \vert Y)</math> and the class [[Prior probability|prior]] <math>\Pr(Y)</math> are found, and the conditional distribution <math>\Pr (Y \vert X)</math> is derived using [[Bayes' theorem|Bayes' rule]].<ref name="bishop"/>{{rp|43}}

==Probability calibration==