Table 3 Performance metrics of various regression models for biomass estimation, including \({R}^2\), RMSE, and MAE. The Stacking model achieves the highest accuracy, demonstrating its superior effectiveness in enhancing predictive performance compared with individual machine learning models (best results are highlighted in bold, and the second-best results are italics).
Number | Model | \({R}^2\) | RMSE | MAE |
|---|---|---|---|---|
1 | RF | 0.72 | 51.59 | 31.91 |
2 | GBR | 0.71 | 52.17 | 33.01 |
3 | SVR | 0.59 | 61.72 | 38.37 |
4 | KNN | 0.53 | 66.52 | 41.18 |
5 | DT | 0.45 | 71.66 | 42.65 |
6 | AdaBoost | 0.62 | 59.79 | 44.17 |
7 | MLP | 0.59 | 62.21 | 40.73 |
8 | Stacking model | 0.73 | 50.63 | 31.27 |
