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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Related methods
Referenced by
Active Learning Logistic RegressionBayesian k-nearest neighborsDecision TreeDempster-Shafer TheoryEnsemble Naive BayesExplainable K-Nearest NeighborsExplainable Naive BayesFastTextK-Nearest NeighborsLinear Discriminant AnalysisLinear Discriminant Analysis (Classification)Logistic regression (ML)Online Naive BayesQuadratic Discriminant AnalysisRegularized Logistic RegressionRegularized Naive BayesSelf-supervised Naive BayesSemi-supervised Naive BayesSupport Vector Machine