Thickness (d) Regression on EllipBench
0.807MAEDCFM
Evaluation Results
| Method | Links | |||
|---|---|---|---|---|
| DCFMMethodological Paradigm=Physics Informed Method2024.07 | 0.807 | 1.192 | 0.829 | |
| Random ForestMethodological Paradigm=Ensemble Learning Method2024.07 | 0.955 | 1.444 | 0.749 | |
| TransformerMethodological Paradigm=Deep Learning Method2024.07 | 1.006 | 1.443 | 0.749 | |
| MLPMethodological Paradigm=Deep Learning Method2024.07 | 1.066 | 1.483 | 0.735 | |
| LightGBMMethodological Paradigm=Ensemble Learning Method2024.07 | 1.264 | 1.685 | 0.659 | |
| XGBoostMethodological Paradigm=Ensemble Learning Method2024.07 | 1.287 | 1.713 | 0.648 | |
| PINNMethodological Paradigm=Physics Informed Method2024.07 | 1.396 | 1.838 | 0.594 | |
| Support Vector RegressorMethodological Paradigm=Conventional Regression Method2024.07 | 1.638 | 2.181 | 0.428 | |
| Ridge RegressionMethodological Paradigm=Conventional Regression Method2024.07 | 2.097 | 2.52 | 0.237 | |
| ElasticNet RegressionMethodological Paradigm=Conventional Regression Method2024.07 | 2.097 | 2.52 | 0.237 |