To eliminate this problem, it was decided to use regularization on the regression. This was done using ridge regression where an additional parameter α which adds a penalty for the size of coefficients equal to the sum of squares. This method is equivalent to ordinary least-squares regression for α = 0. To determine the optimal value of α, we looked at the weighted RMSE in the validation as a function of α for a single combination at a time. This plot shows that the optimal number of parameters for ωB97X-osUW12-D3(BJ) is around 15. From this we investigated the lowest error functionals. We decided to use the lowest error 15 parameter functional for which no B97 components were skipped, which resulted in a final functional form containing the parameters: