Table 25 ML models performance indices [9]
From: A review of optimization techniques and algorithms used for FRP applications in civil engineering
Models | Training set | Test set | ||||
|---|---|---|---|---|---|---|
MAPE (%) | RMSE (kN.m) | R2(%) | MAPE (%) | RMSE (kN.m) | R2(%) | |
CART | 8.54 | 5.83 | 98.5 | 20.77 | 20.91 | 83.5 |
AdaBoost | 4.96 | 3.10 | 99.6 | 8.96 | 6.91 | 98.2 |
GBDT | 1.84 | 1.39 | 99.9 | 8.12 | 6.37 | 98.5 |
XGBoost | 1.44 | 0.80 | 100 | 7.33 | 6.33 | 98.5 |
Super-learner | 1.82 | 1.61 | 99.9 | 6.63 | 5.61 | 98.8 |