Naive Bayes

NaiveBayes, a robust machine learning project developed in Haskell that leverages parallel computing to boost the performance of a Naive Bayes classifier. The project focuses on implementing efficient strategies for model training, k-fold cross-validation, and feature selection. By harnessing the power of parallel processing, NaiveBayes offers a scalable and high-performance solution for classification tasks. Whether you are a Haskell enthusiast, a machine learning practitioner, or anyone interested in exploring the synergy between functional programming and parallel computing, NaiveBayes provides a compelling showcase of how these technologies can be seamlessly integrated to accelerate the training and evaluation of machine learning models. Dive into the code, explore the parallel strategies, and witness the potential of Haskell in the realm of data science.