Quantile Regression on Scenario 3 n=10000
0.6307MSE (tau=0.05)ConquerNet
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| ConquerNetModel=Model B, Kernel=Uniform2026.05 | 0.6307 | 0.2177 | 0.1462 | 0.2086 | 0.6415 | |
| ConquerNetModel=Model B, Kernel=Gaussian2026.05 | 0.6785 | 0.2169 | 0.147 | 0.2303 | 0.612 | |
| ConquerNetModel=Model B, Smoothing Kernel=Uniform2026.05 | 0.7167 | 0.2409 | 0.1372 | 0.2493 | 0.692 | |
| ConquerNetModel=Model B, Kernel=Epanechnikov2026.05 | 0.7278 | 0.2058 | 0.1471 | 0.2277 | 0.5911 | |
| BaselineModel=Model B2026.05 | 0.7292 | 0.2358 | 0.1554 | 0.2451 | 0.6422 | |
| ConquerNetModel=Model B, Smoothing Kernel=Epanechnikov2026.05 | 0.7502 | 0.2454 | 0.139 | 0.2315 | 0.6683 | |
| ConquerNetModel=Model A, Kernel=Gaussian2026.05 | 0.7556 | 0.2403 | 0.1515 | 0.2281 | 0.6931 | |
| ConquerNetModel=Model A, Kernel=Epanechnikov2026.05 | 0.7679 | 0.2242 | 0.1531 | 0.2232 | 0.7798 | |
| BaselineModel=Model B2026.05 | 0.7786 | 0.2306 | 0.1391 | 0.238 | 0.7249 | |
| BaselineModel=Model A2026.05 | 0.7821 | 0.2505 | 0.1708 | 0.2539 | 0.7933 | |
| ConquerNetModel=Model A, Kernel=Uniform2026.05 | 0.8147 | 0.2228 | 0.1468 | 0.2301 | 0.7264 | |
| ConquerNetModel=Model A, Smoothing Kernel=Epanechnikov2026.05 | 0.823 | 0.2129 | 0.1349 | 0.2206 | 0.7998 | |
| ConquerNetModel=Model B, Smoothing Kernel=Gaussian2026.05 | 0.8256 | 0.2518 | 0.1416 | 0.2444 | 0.7536 | |
| ConquerNetModel=Model A, Smoothing Kernel=Gaussian2026.05 | 0.8395 | 0.204 | 0.1295 | 0.2145 | 0.7477 | |
| ConquerNetModel=Model A, Smoothing Kernel=Uniform2026.05 | 0.879 | 0.2064 | 0.13 | 0.2146 | 0.7824 | |
| BaselineModel=Model A2026.05 | 0.9232 | 0.242 | 0.1459 | 0.2413 | 0.996 |