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LogoutR-squared shows how well the data fit the regression model (the goodness of fit). R-squared can take any value between 0 to 1.
R-Squared is also termed the standardized version of MSE. R-squared represents the fraction of variance of the actual value of the response variable captured by the regression model rather than the MSE which captures the residual error.
R-Squared = 1 – (SSE/SST)
R-Squared = SSR/SST
It is recommended to use R-Squared or rather adjusted R-Squared for evaluating the model performance of the regression models. This is primarily because R-Squared captures the fraction of variance of actual values captured by the regression model and tends to give a better picture of the quality of the regression model. Also, MSE values differ based on whether the values of the response variable are scaled or not. A better measure instead of MSE is the root mean squared error (RMSE) which takes care of the fact related to whether the values of the response variable are scaled or not.