Naive Bayes Classifier

A Naive Bayes classifier applies Bayes' theorem to compute the probability of a class given observed features, under the 'naive' assumption that features are conditionally independent given the class label. Despite this rarely true assumption, Naive Bayes performs surprisingly well on text classification tasks because the violation is often mild and the model requires very little training data. It remains a strong, interpretable baseline for spam detection and sentiment analysis.