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I read in a blog that the decision tree has this disadvantage : Not fit for continuous variables:

Can you explain me why please?

Thank you

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Decision trees work well with categorical variables because of the node structure of a tree. A categorical variable can be easily split at a node. For example, yes or no or 0 or 1. A continuous variable is just that, continuous along a range which cannot be easily split at a node. For example, a floating point number between 0 and 100.

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  • $\begingroup$ Also the ordering of the categorical variables value matters to some extent also $\endgroup$ – Aditya May 11 '18 at 1:43
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It can't be used for micro trend detection as each node looks for values greater than or smaller than the threshold value.

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