Naive Bayes Classifier, or Machine Learning for the Poor ;)

Naive Bayes is a widely used method in machine learning, especially in classification tasks. It proves to be particularly effective for problems such as sentiment analysis, spam filtering, and document classification. The algorithm is based on the assumption that features are independent from one another, which maintains both simplicity and computational efficiency. The article discusses several variants of Naive Bayes, including the classic Gaussian model and the Bernoulli variant, which is more suited for binary data. Experiments conducted on various datasets validate that Naive Bayes performs surprisingly well, even when the independence assumption does not hold in practice. Thus, despite its limitations, this algorithm remains popular in the machine learning community.