Research map / Learning theory and algorithms / Prediction and decision losses
Variable Importance Using Decision Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use DSTUMP instead of Lasso/SIS under linear uncorrelated design | SIS/Lasso · simulated linear model, uncorrelated design | support recovery fraction | Hurt |
| use DSTUMP instead of SIS under mildly correlated design | SIS · simulated linear model, correlated design | support recovery fraction | No clear effect |
| use DSTUMP instead of SIS and Lasso with exponential nonlinearity | SIS/Lasso · simulated additive exp model | support recovery fraction | Helped |
| use TREEWEIGHT (multi-level trees) instead of DSTUMP with interaction terms | DSTUMP · simulated interaction models | support recovery fraction | Helped |
| use impurity-based TREEWEIGHT instead of permutation importance | TREEWEIGHT-permutation · simulated models | support recovery fraction | Helped |
Closest papers by meaning
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- Learning Small Decision Trees with Few Outliers: A Parameterized Perspective (AAAI 2024 · similarity 0.85)
- Invariant Random Forest: Tree-Based Model Solution for OOD Generalization (AAAI 2024 · similarity 0.84)
- Variable Importance in High-Dimensional Settings Requires Grouping (AAAI 2024 · similarity 0.84)
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