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I have a raw unlabeled dataset, and I want to design a model to perform a regression. In my dataset, it does not make sense to give each observation a value, but it does make sense to sort them. Can I implement an algorithm to create values for each observation by sorting them?

I thought about this:

  1. Select N random observations and sort them
  2. Give each observation a new score, equal to its position
  3. Calculate the score of an observation as the average position across all times the observation was picked
  4. return to step 1

Does it make sense? Is there any machine learning branch that studies this kind of scenarios?

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There is a field for this called ordinal regression. Does each unique observation have it's own rank or can observations share a rank? IE. if you have 10 elements, are they labeled 1,2,3 ... 10? or could it be 1,1,1, 2,2,2,3,3,3,4? can't post as comment due to karma What are these values supposed to represent? Why are you doing this analysis?

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  • $\begingroup$ Many thanks for your help. Each observation could have its own rank or share a rank. It is an arbitrary decision. I guess it is simpler to suppose each one have its own rank $\endgroup$ Commented Oct 5, 2022 at 6:22
  • $\begingroup$ So are you trying to create a model to pit item A vs item B and decide which is higher ranked? Using the attributes to explain why item A or B is higher ranked? $\endgroup$ Commented Oct 5, 2022 at 13:08
  • $\begingroup$ Yes to the first question. No to the second: I don't need an explanation of why A>B. $\endgroup$ Commented Oct 11, 2022 at 10:38
  • $\begingroup$ Will you always be comparing just two elements at a time? $\endgroup$ Commented Oct 11, 2022 at 13:08
  • $\begingroup$ No, I thought about comparing in batches of 10 elements (N=10). $\endgroup$ Commented Oct 12, 2022 at 15:56

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