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What is the hypothesis space for an Optimal Bayes' Classifier and why do the assumptions of a Naive Bayes' Classifier (that features are conditionally independent of each other) narrow the search space of possible hypothesis?

It seems like the hypothesis space for an Optimal Bayes' Classifier is harder to grasp than something like a Decision Tree - where the hypothesis space is the set of all decision trees possible.

I'm pretty new to ML and this site in general - sorry, if my post doesn't meet requirements, etc.

Thanks!

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