Fig. 2

Performance comparison of the ensemble and individual models on classification tasks. We evaluated our ensemble and individual models across 30 random seeds on the classification datasets to validate the robustness and generalization of the ensemble method. Each e-seed n model was independently trained on different training and validation datasets splitting with different e-seed values. The Ensemble model used soft voting over the six e-seed n models. The test dataset was fixed, and all models were evaluated on the same test dataset on a specific seed value. Notable variations in AUROC values were observed among the e-seed n models. The Ensemble model consistently achieved the best performances across all datasets. Significant improvements of 0.009, 0.018, 0.020, 0.016, 0.022, and 0.022 AUROC over the second-highest scores were observed in BBBP, Tox21, ToxCast, SIDER, ClinTox, and BACE, respectively