Research map / Optimization / Implicit bias and generalization
Learning concise representations for regression by evolving networks of trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FEAT instead of MLP, random forest, Kernel Ridge, or ElasticNet | FEAT · 100 OpenML regression problems | CV R² | Helped |
| Use FEAT instead of XGBoost | FEAT · 100 OpenML regression problems | CV R² | No clear effect |
| Use FEAT instead of XGBoost, random forest, or MLP | FEAT · 100 OpenML regression problems | model size (node count) | Helped |
| Add a correlation-minimization objective (ablation) | FEAT (FeatCorr) · 100 OpenML regression problems | feature correlation | Mixed |
| Add a condition-number objective (ablation) | FEAT (FeatCN) · 100 OpenML regression problems | feature correlation | Mixed |
| Add a correlation-minimization objective (ablation) | FEAT (FeatCorr) · 100 OpenML regression problems | CV R²: 1.00 | No clear effect |
| Add a condition-number objective (ablation) | FEAT (FeatCN) · 100 OpenML regression problems | CV R²: 5.34e-01 | No clear effect |
| Use Lex selection with NSGA2 survival instead of the other tested stochastic-optimization approaches (ablation) | FEAT · 88 PMLB regression datasets | CV R² | Helped |
| Use NSGA2, simulated annealing, or random search instead of LexNSGA2 (ablation) | FEAT · 88 PMLB regression datasets | CV R² | Hurt |
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