Regression on California Housing (test)
0.2002RMSELog Cosh
Evaluation results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Log CoshLoss Function=Log Cosh, Model=Feedforward Network, Optimizer=Adam, Batch Size=32, Epochs=30, Trials=50, Preprocessing=Min-max normalization2026.06 | 0.2002 | — | — | — | 0.1697 | — | — | — | |
| Huber LossLoss Function=Huber Loss, Model=Feedforward Network, Optimizer=Adam, Batch Size=32, Epochs=30, Trials=50, Preprocessing=Min-max normalization2026.06 | 0.2004 | — | — | — | 0.17 | — | — | — | |
| SMAELoss Function=SMAE, Model=Feedforward Network, Optimizer=Adam, Batch Size=32, Epochs=30, Trials=50, Preprocessing=Min-max normalization2026.06 | 0.2016 | — | — | — | 0.1696 | — | — | — | |
| SRLLoss Function=SRL, Model=Feedforward Network, Optimizer=Adam, Batch Size=32, Epochs=30, Trials=50, Preprocessing=Min-max normalization2026.06 | 0.2057 | — | — | — | 0.1691 | — | — | — | |
| MSELoss Function=MSE, Model=Feedforward Network, Optimizer=Adam, Batch Size=32, Epochs=30, Trials=50, Preprocessing=Min-max normalization2026.06 | 0.2088 | — | — | — | 0.1779 | — | — | — | |
| XGB Baseline2026.05 | 0.477 | 0.827 | — | — | 0.316 | 0 | — | — | |
| LGB Baseline2026.05 | 0.477 | 0.826 | — | — | 0.319 | 0 | — | — | |
| CfC#params=14053, Time=3 s2026.05 | 0.58 | — | — | — | — | — | — | — | |
| NODE#params=678, Time=11 min 20 s2026.05 | 0.58 | — | — | — | — | — | — | — | |
| GAM (rmse)Seed=225, Optimization=lowest RMSE2026.02 | 0.6228 | — | — | — | — | — | — | — | |
| GAM (rmse)Seed=729, Optimization=lowest RMSE2026.02 | 0.6496 | — | — | — | — | — | — | — | |
| LinearGAMSeed=7292026.02 | 0.652 | — | — | — | — | — | — | — | |
| GAM (rmse)Seed=123, Optimization=lowest RMSE2026.02 | 0.6523 | — | — | — | — | — | — | — | |
| LinearGAMSeed=2252026.02 | 0.6536 | — | — | — | — | — | — | — | |
| GAM (rmse)Seed=7, Optimization=lowest RMSE2026.02 | 0.6554 | — | — | — | — | — | — | — | |
| PMNN#params=25, Time=54 s2026.05 | 0.66 | — | — | — | — | — | — | — | |
| GAM (knee)Seed=123, Optimization=knee (best trade-off)2026.02 | 0.6663 | — | — | — | — | — | — | — | |
| LinearGAMSeed=72026.02 | 0.6674 | — | — | — | — | — | — | — | |
| LinearGAMSeed=1232026.02 | 0.6697 | — | — | — | — | — | — | — | |
| GAM (knee)Seed=225, Optimization=knee (best trade-off)2026.02 | 0.6702 | — | — | — | — | — | — | — | |
| GAM (knee)Seed=7, Optimization=knee (best trade-off)2026.02 | 0.6745 | — | — | — | — | — | — | — | |
| Decision TreeSeed=2252026.02 | 0.677 | — | — | — | — | — | — | — | |
| Decision TreeSeed=7292026.02 | 0.6801 | — | — | — | — | — | — | — | |
| GAM (rmse)Seed=42, Optimization=lowest RMSE2026.02 | 0.6821 | — | — | — | — | — | — | — | |
| GAM (knee)Seed=729, Optimization=knee (best trade-off)2026.02 | 0.689 | — | — | — | — | — | — | — | |
| Decision TreeSeed=422026.02 | 0.6925 | — | — | — | — | — | — | — | |
| Decision TreeSeed=1232026.02 | 0.6961 | — | — | — | — | — | — | — | |
| GAM (penalty)Seed=729, Optimization=lowest complexity penalty2026.02 | 0.7108 | — | — | — | — | — | — | — | |
| GAM (knee)Seed=42, Optimization=knee (best trade-off)2026.02 | 0.7125 | — | — | — | — | — | — | — | |
| GAM (penalty)Seed=123, Optimization=lowest complexity penalty2026.02 | 0.713 | — | — | — | — | — | — | — | |
| Decision TreeSeed=72026.02 | 0.7159 | — | — | — | — | — | — | — | |
| LinearGAMSeed=422026.02 | 0.7216 | — | — | — | — | — | — | — | |
| GAM (penalty)Seed=7, Optimization=lowest complexity penalty2026.02 | 0.7246 | — | — | — | — | — | — | — | |
| GAM (penalty)Seed=42, Optimization=lowest complexity penalty2026.02 | 0.74 | — | — | — | — | — | — | — | |
| GAM (penalty)Seed=225, Optimization=lowest complexity penalty2026.02 | 0.7824 | — | — | — | — | — | — | — | |
| AdamOptimizer=Adam2024.08 | — | — | — | — | — | — | 22.1 | — | |
| Adam-IGNDOptimizer=Adam-IGND2024.08 | — | — | — | — | — | — | 21.3 | — | |
| CARTDepth=5, Thresholds=202026.06 | — | 0.62 | — | — | — | — | — | 1.06 | |
| CholeskyTreeDepth=5, Thresholds=202026.06 | — | 0.62 | — | — | — | — | — | 1.06 | |
| CLARITreeDepth=5, Thresholds=202026.06 | — | 0.64 | — | — | — | — | — | 1 | |
| Co-EvolvedStrategy=Co-Evolved Ensemble2026.06 | — | — | — | — | 0.887 | — | — | — | |
| CQR1 - alpha=90%2026.05 | — | — | 89.88 | 165,865.4 | — | — | — | — | |
| CQR1 - alpha=85%2026.05 | — | — | 84.94 | 136,485.2 | — | — | — | — | |
| CQR1 - alpha=80%2026.05 | — | — | 80.2 | 116,708 | — | — | — | — | |
| Decision TreeRank=9, Notes=Single tree2026.05 | — | 0.608 | — | — | — | — | — | — | |
| Gradient BoostingRank=3, Notes=Ensemble2026.05 | — | 0.783 | — | — | — | — | — | — | |
| GUIDEDepth=5, Thresholds=202026.06 | — | 0.62 | — | — | — | — | — | 1.06 | |
| IGNDOptimizer=IGND2024.08 | — | — | — | — | — | — | 22.7 | — | |
| K-Nearest NeighboursRank=5, Notes=Distance-based2026.05 | — | 0.668 | — | — | — | — | — | — | |
| Lasso RegressionRank=8, Notes=Sparse OLS2026.05 | — | 0.65 | — | — | — | — | — | — | |
| LinearBase Model=Linear2026.05 | — | 0.576 | — | — | — | — | — | — | |
| Linear RegressionRank=7, Notes=Parametric baseline2026.05 | — | 0.65 | — | — | — | — | — | — | |
| Linear+MARICLBase Model=Linear, Method Enhancement=MARICL2026.05 | — | 0.648 | — | — | — | — | — | — | |
| Post-EvolvedStrategy=Post-Evolved Ensemble2026.06 | — | — | — | — | 1.02 | — | — | — | |
| Random ForestRank=2, Notes=Ensemble2026.05 | — | 0.814 | — | — | — | — | — | — | |
| Ridge RegressionRank=6, Notes=Regularised OLS2026.05 | — | 0.651 | — | — | — | — | — | — | |
| Scaled-score1 - alpha=90%2026.05 | — | — | 91.1 | 130,871.3 | — | — | — | — | |
| Scaled-score1 - alpha=85%2026.05 | — | — | 86.93 | 111,895.6 | — | — | — | — | |
| Scaled-score1 - alpha=80%2026.05 | — | — | 82.39 | 97,614.6 | — | — | — | — | |
| SGDOptimizer=SGD2024.08 | — | — | — | — | — | — | 26.7 | — | |
| Single Net.Strategy=Single Network2026.06 | — | — | — | — | 1.007 | — | — | — | |
| Skew-adaptive1 - alpha=90%2026.05 | — | — | 90.47 | 125,558.5 | — | — | — | — | |
| Skew-adaptive1 - alpha=85%2026.05 | — | — | 86.33 | 104,747.2 | — | — | — | — | |
| Skew-adaptive1 - alpha=80%2026.05 | — | — | 83.49 | 93,677.1 | — | — | — | — | |
| STreeDDepth=5, Thresholds=20, Exceeded training time limit (600s)=true2026.06 | — | 0.61 | — | — | — | — | — | 1.08 | |
| SVR-Linear KernelRank=10, Notes=3k-subset training2026.05 | — | 0.511 | — | — | — | — | — | — | |
| SVR-RBF (Tuned)Rank=4, Notes=This work2026.05 | — | 0.723 | — | — | — | — | — | — | |
| XGB+MARICLBase Model=XGBoost, Method Enhancement=MARICL2026.05 | — | 0.861 | — | — | — | — | — | — | |
| XGBoostRank=1, Notes=Ensemble2026.05 | — | 0.832 | — | — | — | — | — | — | |
| XGBoostBase Model=XGBoost2026.05 | — | 0.832 | — | — | — | — | — | — |