A Naive Bayes classifier is a probabilistic classification model based on Bayes' theorem and a simplifying assumption that features are conditionally independent given the class. Despite this strong assumption, it is often effective for text classification, spam detection, and other high-dimensional tasks.
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.