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  2. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    Example of a naive Bayes classifier depicted as a Bayesian Network. In statistics, naive Bayes classifiers are a family of linear "probabilistic classifiers" which assumes that the features are conditionally independent, given the target class. The strength (naivety) of this assumption is what gives the classifier its name.

  3. Bayesian classifier - Wikipedia

    en.wikipedia.org/wiki/Bayesian_classifier

    a Bayes classifier, one that always chooses the class of highest posterior probability in case this posterior distribution is modelled by assuming the observables are independent, it is a naive Bayes classifier

  4. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    Naive Bayes is a successful classifier based upon the principle of maximum a posteriori (MAP). This approach is naturally extensible to the case of having more than two classes, and was shown to perform well in spite of the underlying simplifying assumption of conditional independence .

  5. Bayesian programming - Wikipedia

    en.wikipedia.org/wiki/Bayesian_programming

    It can be drastically simplified by assuming that the probability of appearance of a word knowing the nature of the text (spam or not) is independent of the appearance of the other words. This is the naive Bayes assumption and this makes this spam filter a naive Bayes model. For instance, the programmer can assume that:

  6. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    This statistics -related article is a stub. You can help Wikipedia by expanding it.

  7. Linear classifier - Wikipedia

    en.wikipedia.org/wiki/Linear_classifier

    Naive Bayes classifier with multinomial or multivariate Bernoulli event models. The second set of methods includes discriminative models, which attempt to maximize the quality of the output on a training set. Additional terms in the training cost function can easily perform regularization of the final model. Examples of discriminative training ...

  8. Loss functions for classification - Wikipedia

    en.wikipedia.org/wiki/Loss_functions_for...

    A loss function is said to be classification-calibrated or Bayes consistent if its optimal is such that / = ⁡ (()) and is thus optimal under the Bayes decision rule. A Bayes consistent loss function allows us to find the Bayes optimal decision function f ϕ ∗ {\displaystyle f_{\phi }^{*}} by directly minimizing the expected risk and without ...

  9. Bayesian linear regression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_linear_regression

    Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often ...