Next to classical metrics like the mean absolute error (MAE), the mean squared error (MSE), and the root mean squared error (RMSE) , SpectraFit also provides used R 2 score and explained variance score. The R 2 , also known as the coefficient of determination , the score is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination or the coefficient of multiple determination for multiple regression. The best possible score for R 2 would be 1.0, and vice versa, the worst 0.0. In the case of a value of R 2 =1.0, the fit model explains all the variability of the dependent dataset (spectrum). The explained variance score (EVS) is a regression score function and works similarly to the R 2 score. 101-103 Again, the best possible score is 1.0, and the lower values are worse. It is important to note that the EVS relies on biased variance, whereas R 2 uses raw sums of squares. It is an ongoing discussion 101, 105 about using the EVS score or R 2 ; however, the maintainer of Scikit-Learn recommends using R 2 over EVS, because R 2 takes a systematic offset in the prediction into account. 106