Kernel ridge regression (KRR) models were implemented using the KernelRidge function in Scikit-Learn. Linear and radial basis function (RBF) kernels were tested, with 5-fold cross validation used to optimise the regularization and kernel width hyper-parameters in the logarithmic ranges 10 0 -10 -3 (4 steps) and 10 -2 -10 2 (50 steps), respectively. Minimum MAE was achieved with a linear kernel and regularization value of 1.