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I am looking for some insight on making a model with just one predictor. Let's assume the data are not linearly separable (because otherwise I assume it wouldn't matter which linear method to use).

What would make a good or bad model other than the classification accuracy (or other metric for that matter). Are there methods (eg neural nets) which don't make sense at all to be used with one predictor?

I don't have any code or a particular example since it is a general question.

This was marked as duplicate of a clustering question, but mine is about supervised learning so I believe it is not the same

PS: thanks @karolis for the edit!

1234
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    Possible duplicate of [How can I group numerical data into naturally forming "brackets"? (e.g. income)](https://stats.stackexchange.com/questions/67571/how-can-i-group-numerical-data-into-naturally-forming-brackets-e-g-income) – Laksan Nathan Jun 02 '19 at 09:30
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    It doesn't look like a duplicate to me since I am asking about supervised learning and the linked question is about clustering – 1234 Jun 02 '19 at 10:55

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