What does 'naive' in Naive Bayes refer to?

The term 'naive' in Naive Bayes specifically refers to the assumption of independence among features. This assumption simplifies the calculations involved in determining the probability of a given class based on the feature set. In practice, it means that when predicting the class of an instance, the algorithm assumes that the presence or absence of a particular feature does not affect the presence or absence of any other feature—each feature contributes independently to the probability of the outcome.

This independence assumption is often too strong in real-world cases—many features may be correlated. However, despite this naive assumption, Naive Bayes classifiers can perform surprisingly well for many tasks, especially in text classification and spam detection. The algorithm’s efficiency stems from this simplification, allowing it to perform quickly and effectively even with large datasets. Thus, the core concept behind 'naive' hinges on the assumption that all features are considered independently, which significantly influences both the model's structure and its performance.