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  2. Naive Bayes classifier - Wikipedia

    en.wikipedia.org/wiki/Naive_Bayes_classifier

    In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. [3] All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method.

  3. 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 .

  4. Bag-of-words model in computer vision - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model_in...

    The simplest one is Naive Bayes classifier. [2] Using the language of graphical models, the Naive Bayes classifier is described by the equation below. The basic idea (or assumption) of this model is that each category has its own distribution over the codebooks, and that the distributions of each category are observably different.

  5. 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 ...

  6. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    Naive Bayes classifier; References This page was last edited on 17 December 2024, at 03:38 (UTC). Text is available under the ... Code of Conduct; Developers;

  7. Approximate Bayesian computation - Wikipedia

    en.wikipedia.org/wiki/Approximate_Bayesian...

    Engine for Likelihood-Free Inference. ELFI is a statistical software package written in Python for Approximate Bayesian Computation (ABC), also known e.g. as likelihood-free inference, simulator-based inference, approximative Bayesian inference etc. [83] ABCpy: Python package for ABC and other likelihood-free inference schemes.

  8. Caffe (software) - Wikipedia

    en.wikipedia.org/wiki/Caffe_(software)

    Download QR code; Print/export Download as PDF; ... Naive Bayes; Artificial neural networks; ... with a Python interface. [5]

  9. Generalized additive model - Wikipedia

    en.wikipedia.org/wiki/Generalized_additive_model

    gam, Python module in statsmodels.gam module. InterpretML, a Python package for fitting GAMs via bagging and boosting. mgcv, an R package for GAMs using penalized regression splines. mboost, an R package for boosting including additive models. gss, an R package for smoothing spline ANOVA. INLA software for Bayesian Inference with GAMs and more.