学习笔记之Naive Bayes Classifier

最新推荐文章于 2022-10-02 09:31:55 发布

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最新推荐文章于 2022-10-02 09:31:55 发布

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原文链接：http://www.cnblogs.com/pegasus923/p/10470230.html

本文介绍了朴素贝叶斯分类器的基本概念，一种基于贝叶斯定理的简单概率分类器，它假设特征之间相互独立。文章讨论了其在文本分类、自动医疗诊断等领域的应用，并强调了其高度可扩展性和线性时间复杂度的优势。

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Naive Bayes classifier - Wikipedia

https://en.wikipedia.org/wiki/Naive_Bayes_classifier
In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes has been studied extensively since the 1960s. It was introduced (though not under that name) into the text retrieval community in the early 1960s,^[1] and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines.^[2] It also finds application in automatic medical diagnosis.^[3]
Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression,^[4]^:718 which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.
In the statistics and computer science literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes.^[5] 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]^[5]