Fig. 3
From: MultiChem: predicting chemical properties using multi-view graph attention network
Performance comparison of the ensemble and individual models on regression tasks. We evaluated our ensemble and individual models across 30 random seeds on the regression datasets. Each e-seed n model was independently trained, and the Ensemble model used soft voting over the six e-seed n models. The Ensemble model consistently outperformed across all datasets. Significant improvements of 0.202, 0.060, and 0.047 RMSE over the second-lowest scores were observed in FreeSolv, ESOL, and Lipo, respectively