Table 1.
Pycaret pre-study.
| Model | MAE | MSE | RMSE | R2 | RMSLE | TT (s) |
|---|---|---|---|---|---|---|
| Light Gradient Boosting |
152.6337 | 141,232.3644 | 375.287 | 0.918 | 1.508 | 0.127 |
| Gradient Boosting | 155.4339 | 141,346.4811 | 375.4099 | 0.9179 | 1.6532 | 0.94 |
| CatBoost | 154.6182 | 143,318.3611 | 378.0807 | 0.9168 | 1.6128 | 2.963 |
| Extreme Gradient Boosting |
157.3401 | 148,772.8508 | 385.1605 | 0.9136 | 1.5959 | 1.129 |
| Random Forest | 160.9863 | 152,788.12 10 | 390.4444 | 0.9113 | 1.3707 | 2.217 |
| Extra Trees | 166.4859 | 158,063.3209 | 397.1247 | 0.9082 | 1.3746 | 1.07 |
| K Neighbors Regressor |
171.5040 | 165,489.9156 | 406.3238 | 0.9039 | 1.3991 | 0.024 |
| Ada Boost | 309.1532 | 246,932.7357 | 496.5982 | 0.8566 | 2.0464 | 0.088 |
| Decision Tree | 199.8973 | 277,651.8974 | 526.6256 | 0.8387 | 1.8514 | 0.049 |
| Linear Regression | 385.0205 | 283,773.3672 | 532.5063 | 0.8352 | 2.6316 | 0.425 |
| Bayesian Ridge | 385.0236 | 283,773.3669 | 532.5063 | 0.8352 | 2.6316 | 0.007 |
| Ridge | 385.0258 | 283,773.3562 | 532.5063 | 0.8352 | 2.6315 | 0.006 |
| Least Angle | 385.0206 | 283,773.3667 | 532.5063 | 0.8352 | 2.6316 | 0.008 |
| Lasso | 385.1961 | 283,776.5953 | 532.5101 | 0.8352 | 2.6312 | 0.007 |
| Orthogonal Matching Pursuit |
385.9314 | 284,452.3678 | 533.1459 | 0.8348 | 2.6404 | 0.007 |
| Huber Regressor | 378.314 1 | 295,961.1937 | 543.7595 | 0.8281 | 2.7125 | 0.032 |
| Passive Agg ressive |
378.0183 | 303,116.3767 | 550.2841 | 0.8239 | 2.768 | 0.014 |
| Lasso LeastAngle | 429.8264 | 316,211.4787 | 562.2203 | 0.8163 | 2.5564 | 0.016 |
| Elastic Net | 547.8478 | 444,773.0906 | 666.8626 | 0.7415 | 2.8225 | 0.007 |