I am trying to build a regressor for a dataset which gives info about students' school performance and the probability of getting admitted in the University of their choice.

The first 5 observations look like this :

    GRE_Score  TOEFL_Score  Uni_Rating  LOR   CGPA  Research Chance_of_admit
0      337         118           4      4.5   9.65     1          0.92
1      324         107           4      4.5   8.87     1          0.76
2      316         104           3      3.5   8.00     1          0.72
3      322         110           3      2.5   8.67     1          0.80
4      314         103           2      3.0   8.21     0          0.65

I have build the following regressors so far : linear regressor, knn regressor and a recurrent neural network. ( I will try a few more later. )

So far, in order to chose among my models, I used the "score" method on the test set for the first two regressors ( it returns the $R^2$ for each one of them ) and the "evaluate" method on the test set for the network ( it returns the MeanSquaredError ).

So, keeping in mind that $R^2$ and MeanSquaredError have different formulas, how can I compare my network with the other two models ??

Any help is much appreciated.


1 Answer 1


If you up to a predictive model, you look for a model which performs well on the test set and the metric of interest is the mean squared error which indicates by how much you fail to predict $y$ on average. So don't use $R^2$. Just compare all models based on MSE.

  • $\begingroup$ I replaced OLS regressor with Ridge regressor.. Ok it is clear that I should use MSE to choose among my final models.. I have one more question now, regarding the tuning of the models : The tuning of the network depends on the MSE.. However, when tuning the Ridge and the kNN regressors, the "cross_val_score" method uses the R^2 coefficient to choose among the models.. Is it ok ? Or should I also change the tuning so that it depends also on the MSE ? $\endgroup$
    – batman
    Commented Jan 18, 2020 at 14:18
  • 1
    $\begingroup$ I would tune on MSE $\endgroup$
    – Peter
    Commented Jan 18, 2020 at 19:15

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