在机器学习中，朴素贝叶斯分类器是一系列以假设特征之间强（朴素）独立下运用贝叶斯定理为基础的简单概率分类器。

朴素贝叶斯自20世纪50年代已广泛研究。在20世纪60年代初就以另外一个名称引入到文本信息检索界中， 并仍然是文本分类的一种热门（基准）方法，文本分类是以词频为特征判断文档所属类别或其他（如垃圾邮件、合法性、体育或政治等等）的问题。通过适当的预处理，它可以与这个领域更先进的方法（包括支持矢量机）相竞争。 它在自动医疗诊断中也有应用。

朴素贝叶斯分类器是高度可扩展的，因此需要数量与学习问题中的变量（特征/预测器）成线性关系的参数。最大似然训练可以通过评估一个封闭形式的表达式来完成， 只需花费线性时间，而不需要其他很多类型的分类器所使用的费时的迭代逼近。

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 1950s. It was introduced under a different name into the text retrieval community in the early 1960s, 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 preprocessing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis.