18C5T13 Study Guide - Final Guide: Naive Bayes Classifier, Bayesian Network, Thomas Bayes
9 Aug 2021
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They can predict class membership probabilities such as the probability that a given tuple belongs to a particular class. Classification algorithms have found a simple bayesian classifier known as the na ve bayesian classifier to be comparable in performance with decision tree and selected neural network classifiers. Bayesian classifiers have also exhibited high accuracy and speed when applied to large databases. Na ve bayesian classifiers assume that the effect of an attribute value on a given class is independent of the values of the other attributes. This assumption is called class conditional independence: bayes" theorem. Bayes" theorem is named after thomas bayes, who did early work in probability and decision theory during the 18th century. In bayesian terms, x is considered evidence. it is described by measurements made on a set of n attributes. Let h be some hypothesis such as that the data tuple x belongs to a specified class c.
