In order to derive and validate the bitter/bitterless classification model, the whole dataset is randomly split into two chunks: the dataset for the cross-validation and the test set for the independent validation. The detailed data-splitting scheme is given as follows: 20% of the bitterants and 20% of the non-bitterants (141 bitterants and 118 non-bitterants) randomly selected from the whole dataset are treated as the test set (Dataset-Test), while the rest of them (566 bitter and 474 bitterless compounds) are adopted to train the model with the cross-validation (CV), which is denoted as Dataset-CV. In the five-fold cross-validation, Dataset-CV is randomly split into five chunks. One chunk is employed as the internal validation set (Dataset-Internal-Validation), and the remaining four chunks are combined to form the training set (Dataset-Training). This procedure will be repeated for five times, which is prepared for the five-fold cross-validation. Finally, to reduce the bias from the data-splitting scheme, the whole data-splitting procedure will be repeated for nineteen or three times depending on the different machine-learning methods. More specifically, 19 data-splitting schemes are adopted for the model-training with KNN, SVM, GBM, and RF, while only three data-splitting schemes are used for the model-training with DNN2 and DNN3 that are very computationally expensive.