Linear Regression on Simulated data
0.156Calibration ErrorSandwich Gauss
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Sandwich GaussB=162026.05 | 0.156 | 0 | |
| Sandwich GaussB=⌊0.1 × N⌋2026.05 | 0.156 | 0 | |
| DQ+exactB=16, Loss=Log loss2026.05 | 0.169 | 0.664 | |
| CTB=16, Loss=Log loss2026.05 | 0.171 | 0.672 | |
| DQ+exactB=16, Loss=β-loss (β = 1.5)2026.05 | 0.172 | 0.695 | |
| DQ+exactB=⌊0.1 × N⌋, Loss=Log loss2026.05 | 0.174 | 0.672 | |
| LR+WSB=⌊0.1 × N⌋, Loss=β-loss (β = 1.5)2026.05 | 0.177 | 1.322 | |
| LR+WSB=16, Loss=β-loss (β = 1.5)2026.05 | 0.178 | 1.115 | |
| CTB=⌊0.1 × N⌋, Loss=Log loss2026.05 | 0.179 | 0.975 | |
| DQ+exactB=⌊0.1 × N⌋, Loss=β-loss (β = 1.5)2026.05 | 0.19 | 0.748 | |
| NUTSB=162026.05 | 0.195 | 0.795 | |
| NUTSB=⌊0.1 × N⌋2026.05 | 0.195 | 0.799 | |
| CTB=⌊0.1 × N⌋, Loss=β-loss (β = 1.5)2026.05 | 0.196 | 1.006 | |
| CTB=16, Loss=β-loss (β = 1.5)2026.05 | 0.201 | 0.64 | |
| PosteriorB=16, Loss=Log loss2026.05 | 0.418 | 0.943 | |
| PosteriorB=⌊0.1 × N⌋, Loss=Log loss2026.05 | 0.418 | 0.943 | |
| LR+WSB=⌊0.1 × N⌋, Loss=Log loss2026.05 | 0.517 | 0.996 | |
| LR+WSB=16, Loss=Log loss2026.05 | 0.529 | 0.995 |