In order to learn and predict the regioselectivity of EAS reactions, we explore various regression and classification models with respect to both the experimental and calculated data described above. Initially, a screening of 17 regression models and 13 classification models using PyCaret version 2.3.2 is conducted (details can be found in the supporting information). This allows us to quickly find promising machine learning methods, which are then thoroughly examined in terms of finding optimal hyperparameters. The hyperparameter optimizations are carried out using a tree-structured Parzen estimator (TPE) as implemented in Optuna version 2.5.0. All training and evaluation are done using either a normal or a stratified 5-fold cross-validation of the randomly shuffled training set in the case of the regression and classification models, respectively, and only the models with the best validation performance are saved for testing. As we show in Table the best performance for both regression and classification is the ensemble decision tree variant called light gradient boosting machine (LightGBM) version 3.1.1 34 using the sorted-shell atomic descriptors with a shell radius of 5. We refer to this method as simply "LightGBM" hereafter. Furthermore, we examine the imbalance in the dataset using a "Null model", where all sites are predicted to be non-reactive. And we employ a 1-nearest neighbors (1-NN) classifier as a baseline model using the brute-force search algorithm and the Jaccard metric as implemented in scikit-learn, which corresponds to a perfect memorization of the training set. Table : Comparing different methods for predicting the reactivity of the 14,739 unique reaction sites in the test set and the 27,465 unique reaction sites in the out-of-sample set. The reported metrics are accuracy (ACC), Matthew's correlation coefficient (MCC), precision (PPV or positive predictive value), recall (TPR or true positive rate), specificity (TNR or true negative rate), and negative predictive value (NPV).