The performance of the ML algorithms outlined above were assessed using a nested-cross validation approach. This means that we created ten-folds, which led to a total of ten test sets, each being evaluated by nine different training and validation sets. This generated a total of 90 training-testing processes. This allows assessment of robustness and stability of the different methods evaluated. For evaluation of the prediction of the '%topA' variable, we report the RMSE (%), while for the face of addition, accuracy is used. The finalized performance of each of the ML models (LR, RFR and GBR) using each of the different electronic descriptors evaluated herein are given in Table for all the testing points gathered from the 90 training-testing processes, leading to a total of 927 testing reactions. The best prediction of '%topA' was obtained using the RFR algorithm using the V2 electronic descriptor, with a RMSE value of 9.87%. This low RMSE value shows that the ML algorithm was able to successfully predict selectivity for unseen substrates using easily interpretable chemical features. Results using the GBR algorithm obtained similarly low RMSE values, while the LR algorithm performs the weakest of the three tested algorithms. The representation V2 attained the lowest RMSE values, although the other descriptors performed quite similarly. The small difference between the results from the electronic descriptors V1-V4 suggests that the methods used to estimate the electronics of the aryl unit were consistent to each other.