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I split my data into training set and test set and am running linear regression on it. I am using Python's "scikit" library and I am getting an $R^2$ score of 0.31 and an RMSE value of 0.037. The response variable in my regression consists of values between 0 and 1 so the RMSE value is giving me an error rate of 3.7%. However, the low $R^2$ score indicates that the model is not working well. I'm not sure if I'm misunderstanding these metrics and not sure how to interpret these results. I would appreciate any guidance.

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marked as duplicate by mkt - Reinstate Monica, kjetil b halvorsen, Reinstate Monica, Michael Chernick, user158565 Jun 25 at 2:45

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  • $\begingroup$ stats.stackexchange.com/questions/38631/… This thread might help. $\endgroup$ – tho_mi Dec 5 '15 at 21:15
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    $\begingroup$ Depending on the task, an R^2 of 0.31 may be pretty good. $\endgroup$ – George Dec 5 '15 at 21:28
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    $\begingroup$ What makes that $R^2$ "poor"? What makes some larger $R^2$ "good"? $\endgroup$ – Glen_b Dec 6 '15 at 4:43
  • $\begingroup$ "The response variable in my regression consists of values between 0 and 1 so the RMSE value is giving me an error rate of 3.7%" - I don't think that's a valid interpretation of RMSE... Have you tried just plotting the data? $\endgroup$ – naught101 Sep 2 '16 at 5:50