Table 4 Results of evaluation of models for predicting DTC, DTS, and DTST on training and testing datasets.
Target | Method | Train set (80%) | Test set (20%) | ||||
|---|---|---|---|---|---|---|---|
R2 | MSE | RMSE | R2 | MSE | RMSE | ||
DTC | RF | 0.995 | 0.62 | 0.79 | 0.974 | 3.20 | 1.79 |
GB | 0.940 | 7.22 | 2.69 | 0.933 | 8.21 | 2.87 | |
MPR | 0.853 | 17.81 | 4.22 | 0.846 | 19.03 | 4.36 | |
SVR | 0.818 | 22.04 | 4.70 | 0.815 | 22.78 | 4.77 | |
DTS | RF | 0.997 | 0.93 | 0.96 | 0.977 | 8.84 | 2.97 |
GB | 0.967 | 12.10 | 3.47 | 0.960 | 15.15 | 3.89 | |
MPR | 0.939 | 22.43 | 4.73 | 0.944 | 21.23 | 4.61 | |
SVR | 0.920 | 29.45 | 5.42 | 0.920 | 30.00 | 5.47 | |
MLR | 0.886 | 42.25 | 6.50 | 0.895 | 39.70 | 6.30 | |
DTST | RF | 0.999 | 4.69 | 0.01 | 0.943 | 10.26 | 3.20 |
CatBoost | 0.976 | 4.18 | 2.04 | 0.928 | 12.89 | 3.59 | |
LightGBM | 0.935 | 11.79 | 3.43 | 0.883 | 20.95 | 4.57 | |
ANN | 0.803 | 35.50 | 5.95 | 0.795 | 36.71 | 6.05 | |
