The naive Bayes algorithm requires sample features to be independent and identically distributed. Is this statement true or false?

The statement regarding the naive Bayes algorithm's requirement for sample features to be independent and identically distributed is indeed false. One of the fundamental assumptions of naive Bayes is that the features used in the model are conditionally independent given the class label. This means that while the algorithm assumes that the presence of a feature does not depend on the presence of other features when conditioned on the class, it does not necessitate that the features be independent in all scenarios or that they are identically distributed.

In practice, naive Bayes can perform well even when the independence assumption does not hold, although its performance may be affected by strong correlations among features. The "identically distributed" aspect is not a strict requirement for naive Bayes; it simply requires that each feature given the class is well-defined according to the class distribution, which allows for heterogeneity within different feature distributions.

This characteristic of naive Bayes allows it to be effective for various classification tasks, even when the assumptions may not be wholly accurate, leading to its continued use in many practical applications. Understanding this principle is crucial for effectively applying the naive Bayes algorithm in machine learning contexts.