Why is ‘naïve Bayes’ naïve?

It is naïve because it assumes that all dataset are equally important and independent, which is not the case in a real-world scenario.

The term “naïve” in “naïve Bayes” refers to a simplifying assumption made by the algorithm regarding the independence of features. In other words, it assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. This assumption is called naïve because it’s often unrealistic in real-world scenarios where features may be correlated. Despite its simplicity, naïve Bayes often performs surprisingly well in practice, especially in text classification and other similar tasks, making it a popular choice due to its computational efficiency and ease of implementation. However, it’s important to recognize its limitations and the situations where its naïve assumptions might not hold true.