How can I compare my regressors? 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.
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.