Thus, nine consensus models are proposed based on the balance among the accuracy, speed and diversity of machine-learning methods, and are implicitly integrated in our e-Bitter program. Consensus model 1 (CM01) selects 19 best individual models (Table S5) from Table S1 purely based on the highest F1-scores in each data-splitting scheme. Consensus model 2 (CM02) chooses the average models (AM32, AM28, AM31, AM11, and AM69 in Table S6) considering each machine-learning method with the highest F1-scores to balance the diversity and performance of machine-learning methods. Consensus model 3 (CM03) considers the top average models (AM32, AM26, AM28, AM62, and AM52 in Table S7) with the highest F1-scores. Consensus model 4 (CM04) selects the top five average models (AM31, AM49, AM55, AM67, and AM43 in Table S8) trained with KNN. Consensus model 5 (CM05) comprises the top five average models (AM32, AM26, AM62, AM50, and AM56 in Table S9) trained with SVM. Consensus model 6 (CM06) includes the top five average models (AM69, AM63, AM51, AM33, and AM57 in Table S10) trained with GBM. Consensus model 7 (CM07) combines the top five average models (AM28, AM52, AM10, AM46, and AM70 in Table S11) trained with RF. Consensus model 8 (CM08) consists of the top average models (AM11, AM23, AM35, AM05, and AM29 in Table S12) trained with DNN2. Consensus model 9 (CM09) contains the top average models (AM06, AM36, AM12, AM18, and AM30 in Table S13) trained with DNN3. All the evaluation metrics for each consensus model (Table 1) are obtained by averaging over all the constituent models.