Table 23 Performance metrics for the suggested models [27]
From: A review of optimization techniques and algorithms used for FRP applications in civil engineering
Models | Training dataset | Test dataset | ||||||
|---|---|---|---|---|---|---|---|---|
MAPE (%) | MAE (kN) | RMSE (kN) | R2 | MAPE (%) | MAE (kN) | RMSE (kN) | R2 | |
SVR | 6.69 | 7.97 | 14.55 | 0.968 | 10.01 | 13.76 | 19.29 | 0.952 |
CART | 4.58 | 6.86 | 12.23 | 0.978 | 9.36 | 14.56 | 20 98 | 0.943 |
RFR | 7.09 | 8.60 | 12.21 | 0.978 | 7.96 | 12.82 | 19.48 | 0.951 |
ERT | 3.83 | 4.35 | 7.03 | 0.993 | 7.37 | 11.44 | 16.61 | 0.964 |
GTBR | 3.50 | 4.30 | 6.78 | 0.993 | 8.48 | 10.50 | 13.55 | 0.976 |
xgBoost | 1.84 | 2.62 | 5.94 | 0.995 | 6.16 | 8.23 | 10.96 | 0.984 |