Research map / Learning theory and algorithms / Prediction and decision losses
Generalized and Scalable Optimal Sparse Decision Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace bucketization preprocessing with exact handling of all continuous split points (GOSDT) | GOSDT on continuous datasets · Continuous datasets, training time vs number of binary features | slowdown relative to own best time | Helped |
| Bucketization preprocessing (as used in DL8.5 and BinOCT) | Optimal decision tree training · Constructed 2-D dataset (Figure 1) | training accuracy: 93.5% without bucketization vs 92.2% with | Hurt |
| Add Incremental Similar Support Bound and hash-tree shared subtrees (GOSDT) | GOSDT on continuous variables · Continuous datasets | training time | Helped |
| Optimize AUC (ROC convex hull) objective directly rather than accuracy | GOSDT · FourClass dataset | AUC on training ROC | No clear effect |
Closest papers by meaning
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- Witty: An Efficient Solver for Computing Minimum-Size Decision Trees (AAAI 2025 · similarity 0.89)
- Learning Small Decision Trees with Few Outliers: A Parameterized Perspective (AAAI 2024 · similarity 0.89)
- SORTeD Rashomon Sets of Sparse Decision Trees: Anytime Enumeration (NeurIPS 2025 · similarity 0.89)
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