Machine‐learning models
The calculated features were shuffled and then randomly split into 70% (82 treatment plans) being fed into the training dataset and 30% (36 treatment plans) into the test dataset. In this study, the Scikit‐learn implementation of RFR and SVR with linear kernel was used, which gives the user an option for tuning the hyperparameters for both models. To tune hyperparameters and avoid overfitting, a 10‐fold cross‐validation was conducted. Moreover, the permutation‐importance of the Scikit‐learn was used to demonstrate what variable has the most impact on GPR.