To examine the reliability of all the 1,312 models, Y-randomization test is conducted by the random shuffling of the experimentally-observed labels, which generates a noisy dataset (Table S3). Before the model-training, the accuracy, precision, specificity, sensitivity, F1-score, and MCC of this noisy dataset are 47.5~53.3%, 51.8~57.1%, 42.4~48.7%, 51.8~57.1% 0.518~0.571, and −0.058~0.058 respectively (Table S3), which indicates that almost half of samples are still correct in this noisy dataset. Taken the randomized exp01 (Table S3) as an example, the respective numbers for the true bitterants (TP) and true non-bitterants (TN) are 315 and 223, while the respective numbers for the false bitterants (FP) and false non-bitterants (FN) are 251 and 251 due to the random shuffling (Table S3). However, the ratio between bitterants (TP and FP) and non-bitterants (TN and FN) is still 566:474, which is identical with its counterpart in the original Dataset-CV before the shuffling.