Machine learning

Naive Bayes

Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.

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Sources

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072

Referenced by

Active Learning Logistic Regression Bayesian k-nearest neighbors Decision Tree Dempster-Shafer Theory Ensemble Naive Bayes Explainable K-Nearest Neighbors Explainable Naive Bayes FastText K-Nearest Neighbors Linear Discriminant Analysis Linear Discriminant Analysis (Classification)Logistic regression (ML)Online Naive Bayes Quadratic Discriminant Analysis Regularized Logistic Regression Regularized Naive Bayes Self-supervised Naive Bayes Semi-supervised Naive Bayes Support Vector Machine

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