Does it make sense to have 2 branches resulting in same decision while using one single variable to divide the predictor multiple times? I have been recently come across a problem that entails using a decision tree that only uses one continuous variable to divide the predictor on multiple threshold, while some splits result in the same decision. The decision tree is as follows:

So I have two questions:

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*Does it make sense to use one variable multiple times for dividing the predictor?

*Is it possible that two splits result in the same decision?

I didn't share any codes or data set because I just want to know whether this condition is possible and why we would prefer this.

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*Absolutely.  In one particular case, you may have a non-monotonic relationship between the continuous variable and the target, so that the "correct" tree will have several switches from No to Yes along the leaves from left to right.


*Yes.  While the final "answer" Yes/No can be the same in two leaves from the same parent, their proportions of "yes" samples might be quite different, which may be informative for you.  If at the end of the day you only care about the final decision, you can always post-process the tree to simplify the rules.
