In the case of GPs, all features were scaled using the uniform QuantileTransformer. Scaling was also applied to the target values. Typically, target values were subjected to standard scaling, except in the case of MLP model architectures where min-max was also used. Crucially, the features were scaled based on the values in the training set. Scaling training and test sets together introduces data leakage into the model, which results in overestimation of model performance. 6. ML model optimization and evaluation