Table 3 Hyperparameter tuning and their corresponding performance metrices for SVR.
From: Machine learning based approach for surface roughness prediction in precision dental prototyping
C | Gamma | RMSE | R2 |
|---|---|---|---|
1 | 1 | 0.018083 | 0.96706 |
3 | 4 | 0.043254 | 0.81151 |
2 | 3 | 0.041507 | 0.82643 |
3 | 3 | 0.040258 | 0.83672 |
5 | 4 | 0.042707 | 0.81625 |
1 | 4 | 0.043804 | 0.80668 |
4 | 4 | 0.042985 | 0.81384 |
4 | 3 | 0.039019 | 0.84661 |
5 | 5 | 0.043679 | 0.80779 |
1 | 3 | 0.042759 | 0.8158 |
2 | 2 | 0.027687 | 0.92277 |
3 | 2 | 0.021335 | 0.95414 |
4 | 5 | 0.043752 | 0.80714 |
5 | 5 | 0.043679 | 0.80779 |
2 | 5 | 0.043951 | 0.80538 |
1 | 5 | 0.048843 | 0.75965 |
5 | 3 | 0.037787 | 0.85615 |
4 | 2 | 0.018142 | 0.96684 |
5 | 1 | 0.017974 | 0.96745 |
