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Intro:

Ordinal Regression/Classification is a classification where the labels have orders (https://en.wikipedia.org/wiki/Ordinal_regression)

Question:

Can you comment what are pros and cons if someone wants to solve a regression problem by ordinal regression? Why someone should or shouldn't do that?

Why I am asking:

I work on a regression problem with binned inputs. So, the cardinality of input values is a number like N and thus the cardinality of all output is n << N.

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If the dependent variable is ordinal, then ordinal regression is probably the most advantageous approach in many situations. Disadvantages? 1) It is more complicated to do than a simple hypothesis test. 2) Your audience may not be familiar with it. 3) There are assumptions that should be considered (proportial odds assumption).

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