Table 7.
Performance comparison of machine learning models (Bayesian Ridge, SVR, random Forest, and Ensemble) for each material dataset.
| Material file | Model | RMSE | R 2 |
|---|---|---|---|
| material1.xlsx | Bayesian Ridge | 0.6590 | 0.9172 |
| SVR (RBF) | 1.4145 | 0.6186 | |
| Random Forest | 1.3791 | 0.6375 | |
| Ensemble (Bayesian Ridge + RF) | 0.8708 | 0.8555 | |
| material2.xlsx | Bayesian Ridge | 0.4225 | 0.9717 |
| SVR (RBF) | 1.9582 | 0.3920 | |
| Random Forest | 1.5624 | 0.6129 | |
| Ensemble (Bayesian Ridge + RF) | 0.8313 | 0.8904 | |
| material3.xlsx | Bayesian Ridge | 0.4770 | 0.9592 |
| SVR (RBF) | 1.6227 | 0.5277 | |
| Random Forest | 1.2609 | 0.7148 | |
| Ensemble (Bayesian Ridge + RF) | 0.7102 | 0.9095 | |
| material4.xlsx | Bayesian Ridge | 0.5482 | 0.9506 |
| SVR (RBF) | 1.4973 | 0.6317 | |
| Random Forest | 1.2796 | 0.7310 | |
| Ensemble (Bayesian Ridge + RF) | 0.7533 | 0.9068 | |
| material5.xlsx | Bayesian Ridge | 0.4840 | 0.9591 |
| SVR (RBF) | 1.6691 | 0.5138 | |
| Random Forest | 1.3090 | 0.7010 | |
| Ensemble (Bayesian Ridge + RF) | 0.7626 | 0.8985 | |
| material6.xlsx | Bayesian Ridge | 0.3528 | 0.9804 |
| SVR (RBF) | 1.8202 | 0.4771 | |
| Random Forest | 1.5534 | 0.6191 | |
| Ensemble (Bayesian Ridge + RF) | 0.8456 | 0.8872 | |
| material7.xlsx | Bayesian Ridge | 0.6429 | 0.8807 |
| SVR (RBF) | 0.9700 | 0.7283 | |
| Random Forest | 0.9374 | 0.7463 | |
| Ensemble (Bayesian Ridge + RF) | 0.4983 | 0.9283 |