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No, not quite. _All_ classification algorithms use "reasoning on the average". (A.k.a. "statistics".)

Naive Bayes is a poor classifier because it ignores conditional dependency: when having feature A raises the odds, having feature also B raises the odds, but having features A and B together lowers the odds.

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Known dependencies can be taken into account by treating joint A&B occurrence as another feature. The result can sometimes be significantly improved with this simple hack.

This is an example of why feature engineering is very important, at times more so than the algorithm choice.

Learning to develop features that go together well with an algorithm is essential to practical machine learning.

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