What is Naïve Bayes and why is it called 'naïve'?

Naïve Bayes is a probabilistic classifier based on Bayes’ theorem with the assumption that features are conditionally independent.

Naïve Bayes applies Bayes’ theorem to compute posterior probabilities of classes given input features. The 'naïve' assumption means it assumes all features are independent given the class, which simplifies computation significantly even though it is rarely true in real-world data. Despite this simplification, it performs surprisingly well in text classification and high-dimensional sparse data.