Is Naive Bayes considered a complex classification method?

Naive Bayes is considered a straightforward classification method, and its foundation lies in Bayes' theorem, which is a fundamental principle in probability theory. It operates on the assumption that the features used for classification are independent of each other, which simplifies the computations involved in the classification task. This simplification allows Naive Bayes to be efficiently trained, even on smaller datasets, and provides it with a strong performance in many scenarios, particularly in text classification and spam detection.

The method operates well with categorical data and requires relatively less computational power compared to more complex models. Its reliance on probability and the independence assumption makes it less intricate than many other classification methods, such as deep learning algorithms or support vector machines that handle feature interactions more robustly. Thus, stating that Naive Bayes is derived from Bayes' theorem underscores its fundamental, clear-cut, and accessible nature in the realm of classification techniques.