Nate Cohn

@Nate_Cohn ↗

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Last Checked Feb. 3, 2022

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Wed Aug 12 18:16:06 +0000 2020

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Nate Cohn @Nate_Cohn

@gelliottmorris As someone who likes MRP, surely--surely--you have experience fitting a model, adding more and more and more variables without meaningful change in fit, but nonetheless get more and more outlandlish predictions on unusual cases — PolitiTweet.org

Posted Aug. 12, 2020 Hibernated

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Nate Cohn @Nate_Cohn

@gelliottmorris I don't see what this purports to prove? Just because a model doesn't have a really tight fit doesn't mean it isn't overfit? It's not like you're overfit at +/- 4 but not at 6 or something. It just doesn't really help evaluate this stuff — PolitiTweet.org

Posted Aug. 12, 2020 Hibernated

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Nate Cohn @Nate_Cohn

@gelliottmorris one nice benchmark is the number of variables considered with respect to the sample size, and you're talking about an N=17 or whatever dataset! In the absence of strong theoretical principles, you don't have the data to make durable out-of-sample inferences with a complex model — PolitiTweet.org

Posted Aug. 12, 2020 Hibernated