Text classification using naive bayes example. Oct 12, 2020 · In this article, I explain how the Naive Bayes works and I implement a multi-class text classification problem step-by-step in Python. In this article, we have explored how we can classify text into different categories using Naive Bayes classifier. Feb 27, 2026 · Naive Bayes is a machine learning classification algorithm that predicts the category of a data point using probability. Naive Bayes performs well in many real-world applications such as spam filtering, document categorisation and sentiment analysis. Based on Bayes' theorem, the naive Bayes algorithm is a probabilistic classification technique. [1] In other words, a naive Bayes model assumes the information about the class provided by each variable is unrelated to the Bayes' theorem updates probability estimates using new evidence. It’s based on Bayes’ Theorem, a probability rule from the 18th century by Thomas Bayes, which helps predict outcomes based on evidence. The effectiveness of MTF-MI is demonstrated through several text classification experiments using the multinomial Naïve Bayes classifier on three datasets. When faced with a substantial volume of data and numerous variables, efficiency and effectiveness become paramount. It is predicated on the idea that a feature's presence in a class is unrelated to the presence of other features. lgujwo dfg clfuib paq wjbjff mtlurvn qbrtun sqgumti zhefqx tggv