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  2. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    This section discusses strategies of extending the existing binary classifiers to solve multi-class classification problems. Several algorithms have been developed based on neural networks, decision trees, k-nearest neighbors, naive Bayes, support vector machines and extreme learning machines to address multi-class classification problems ...

  3. 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.

  4. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  5. Linear classifier - Wikipedia

    en.wikipedia.org/wiki/Linear_classifier

    In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features.Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.

  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. Online machine learning - Wikipedia

    en.wikipedia.org/wiki/Online_machine_learning

    scikit-learn: Provides out-of-core implementations of algorithms for Classification: Perceptron, SGD classifier, Naive bayes classifier. Regression: SGD Regressor, Passive Aggressive regressor. Clustering: Mini-batch k-means. Feature extraction: Mini-batch dictionary learning, Incremental PCA.

  8. 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:

  9. Nested sampling algorithm - Wikipedia

    en.wikipedia.org/wiki/Nested_sampling_algorithm

    A NestedSampler is part of the Python toolbox BayesicFitting [9] for generic model fitting and evidence calculation. It is available on GitHub. An implementation in C++, named DIAMONDS, is on GitHub. A highly modular Python parallel example for statistical physics and condensed matter physics uses is on GitHub.