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I am taking a NLP class, in which it says decision tree has the fragmentation problem.

It says Naive Bayes is :

"Very good in domains with many equally important features. Decision Trees suffer from fragmentation in such cases – especially if little data"

However, it doesn't explain about it further.

In my past opinion, decision tree ensembles should outperforms Naive Bayes in any cases since it hold no assumption about data. So I am curious about what it is, but little info I can find at Google.

Could you please explain fragmentation problem to me, thanks.

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    $\begingroup$ Note that decision tree and decision tree ensembles are two different worlds -- fragmentation (basically noise in the near-leaf branches) in ensemble should average out. Single decision tree is mostly about making interpretable and meaningful model, not necessarily perfectly accurate. $\endgroup$
    – user88
    Commented Nov 29, 2014 at 10:56

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