Regression on concrete (test)
0.1092MSECV-PURe
Evaluation results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| CV-PURenoise level (σ)=0.002026.06 | 0.1092 | — | — | — | — | — | — | — | |
| RV-PURenoise level (σ)=0.002026.06 | 0.1095 | — | — | — | — | — | — | — | |
| RV-MLPnoise level (σ)=0.002026.06 | 0.1118 | — | — | — | — | — | — | — | |
| CV-MLPnoise level (σ)=0.002026.06 | 0.113 | — | — | — | — | — | — | — | |
| CV-PURenoise level (σ)=0.052026.06 | 0.1258 | — | — | — | — | — | — | — | |
| RV-PURenoise level (σ)=0.052026.06 | 0.1265 | — | — | — | — | — | — | — | |
| RV-MLPnoise level (σ)=0.052026.06 | 0.1275 | — | — | — | — | — | — | — | |
| CV-MLPnoise level (σ)=0.052026.06 | 0.1304 | — | — | — | — | — | — | — | |
| ParamBoostImplementation=JAX2026.04 | 27.774 | — | — | — | — | — | — | — | |
| EBMType=Tree-based GAM2026.04 | 28.153 | — | — | — | — | — | — | — | |
| APLRType=Piecewise-linear GAM2026.04 | 34.009 | — | — | — | — | — | — | — | |
| NBMArchitecture=100 shared basis functions, Optimizer=AdamW2026.04 | 48.165 | — | — | — | — | — | — | — | |
| NAMArchitecture=MLP (64, 64, 32), Optimizer=AdamW2026.04 | 52.213 | — | — | — | — | — | — | — | |
| Linear/Logistic regressionImplementation=scikit-learn2026.04 | 112.939 | — | — | — | — | — | — | — | |
| MGCVImplementation=PyMGCV, Bases=CubicSpline2026.04 | 113.03 | — | — | — | — | — | — | — | |
| BoostedCPbase_procedure=Local, target miscoverage alpha=10%2024.06 | — | 8.763 | -18.4 | — | — | — | — | — | |
| BoostedCPbase_procedure=CQR, target miscoverage alpha=10%2024.06 | — | 8.265 | -8.56 | — | — | — | — | — | |
| CatBoostFit time (s, mean)=0.12, Seeds=52026.06 | — | — | — | — | — | — | — | 4.3316 | |
| CKMEBase Model=GDN2026.02 | — | — | — | 1.045 | — | — | — | — | |
| CKMEBase Model=MDN2026.02 | — | — | — | 1.032 | — | — | — | — | |
| CKMEBase Model=BNN2026.02 | — | — | — | 1.065 | — | — | — | — | |
| CKMEBase Model=DRF2026.02 | — | — | — | 0.871 | — | — | — | — | |
| Co-EvolvedStrategy=Co-Evolved Ensemble2026.06 | — | — | — | — | — | — | 14.142 | — | |
| CQRtarget miscoverage alpha=10%2024.06 | — | 9.039 | — | — | — | — | — | — | |
| Ensemble-NN2026.03 | — | — | — | — | — | 5.437 | — | — | |
| GradientBoostingFit time (s, mean)=0.24, Seeds=52026.06 | — | — | — | — | — | — | — | 4.7117 | |
| IFlagMethod category=Baseline (no Locus), Base model=IFlag, Target acceptance rate=≈ 70%2026.03 | — | — | — | — | 33.5 | — | — | — | |
| KPPFit time (s, mean)=1.16, Seeds=52026.06 | — | — | — | — | — | — | — | 4.3024 | |
| LightGBMFit time (s, mean)=0.37, Seeds=52026.06 | — | — | — | — | — | — | — | 4.2472 | |
| Localtarget miscoverage alpha=10%2024.06 | — | 10.74 | — | — | — | — | — | — | |
| Locus-BARTMethod category=Locus-Tuned, Base model=BART, Calibration level (alpha)=10%, Target acceptance rate=≈ 70%2026.03 | — | — | — | — | 25.3 | — | — | — | |
| Locus-BART (gamma-infl.)Method category=Locus-Tuned, Base model=BART, Inflation=gamma-infl., Calibration level (alpha)=10%, Target acceptance rate=≈ 70%2026.03 | — | — | — | — | 26.2 | — | — | — | |
| Locus-MC DropoutMethod category=Locus-Tuned, Base model=MC Dropout, Calibration level (alpha)=10%, Target acceptance rate=≈ 70%2026.03 | — | — | — | — | 27.7 | — | — | — | |
| Locus-MC Dropout (gamma-infl.)Method category=Locus-Tuned, Base model=MC Dropout, Inflation=gamma-infl., Calibration level (alpha)=10%, Target acceptance rate=≈ 70%2026.03 | — | — | — | — | 27.4 | — | — | — | |
| MDN2026.03 | — | — | — | — | — | 4.501 | — | — | |
| NE-GMM2026.03 | — | — | — | — | — | 3.649 | — | — | |
| NGBoost2026.03 | — | — | — | — | — | 6.228 | — | — | |
| Post-EvolvedStrategy=Post-Evolved Ensemble2026.06 | — | — | — | — | — | — | 15.749 | — | |
| RandomForestFit time (s, mean)=0.19, Seeds=52026.06 | — | — | — | — | — | — | — | 4.8994 | |
| Ridge_rawFit time (s, mean)=0.00, Seeds=52026.06 | — | — | — | — | — | — | — | 10.7906 | |
| SampleNet2026.03 | — | — | — | — | — | 5.486 | — | — | |
| Single Net.Strategy=Single Network2026.06 | — | — | — | — | — | — | 15.769 | — | |
| VARNetMethod category=Baseline (no Locus), Base model=VARNet, Target acceptance rate=≈ 70%2026.03 | — | — | — | — | 27.7 | — | — | — | |
| XGBoostFit time (s, mean)=0.18, Seeds=52026.06 | — | — | — | — | — | — | — | 4.4086 | |
| β-NLL2026.03 | — | — | — | — | — | 6.125 | — | — |