Naive Bayes

Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. It is not a single algorithm but a family of algorithms where all of them share a common principle, namely that every pair of features being classified is independent of each other. Despite the naive assumption of conditional independence, which is rarely true in real-world data, the classifier often performs surprisingly well, especially for text classification tasks like spam filtering and sentiment analysis. It is computationally efficient and requires a relatively small amount of training data to estimate the necessary parameters.