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In my models, R2 in training and test sets are close to each other, but in RMSE, MSE, MAE of some models, these are very different? what is the reason Is there a solution?

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  • $\begingroup$ Welcome to Data Science! How are you calculating $R^2?$ $\endgroup$
    – Dave
    Commented May 26 at 21:36
  • $\begingroup$ Thank you! I used merics.r2_score $\endgroup$ Commented May 27 at 22:31
  • $\begingroup$ In what sense are the RMSE and MAE bad but the $R^2$ is good? $\endgroup$
    – Dave
    Commented May 27 at 23:44
  • $\begingroup$ Actually, there is a big difference between RMSE in training and test set! Both R2 in test and training are 0.99, but in other metrics the difference is big. which I think shows an overfitting. $\endgroup$ Commented May 28 at 4:14
  • $\begingroup$ I argue here that sklearn.metrics.r2_score is a nonsensical calculation when it comes to out-of-sample evaluation. Do your in-sample and out-of-sample means differ considerably? (Might you have some kind of data drift?) $\endgroup$
    – Dave
    Commented May 28 at 7:59

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