Naïve Bayes classifiers are a family of probabilistic models based on Bayes’ theorem, which is used for classification tasks. They operate under the assumption of independence among features, meaning that the presence of one feature does not affect the presence of another. This “naive” assumption simplifies the computation, making Naïve Bayes classifiers extremely fast in both training and prediction. Despite their simplicity, they perform surprisingly well on various real-world problems, particularly in text classification tasks such as spam detection and sentiment analysis. Their scalability and interpretability further enhance their appeal in machine learning applications.