Naive Bayes
Spam Filtering, Bayesian Approach
This article explains the implementation of a Naive Bayes spam filter, using Bayes' theorem to classify messages as spam or ham. It covers the algorithm’s theoretical foundation, the dataset and implementation details, and the results, which show over 90% accuracy with minimal computational resources. The article also discusses limitations, such as the assumption of word independence and the challenges of imbalanced data, and suggests potential improvements.