Discussion
Why does naive Bayes work despite its false assumption?
Answer: C
Correlated features cause the probabilities to be badly calibrated, often close to zero or one, but the class with the highest score is frequently still the right one, and only the ordering matters for a classification decision. The assumption rarely holds, the errors do not cancel, and training does not correct it. That a model can be wrong in its outputs and right in its decisions is a distinction worth drawing whenever a probability is used only to choose.
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