Research map / Learning theory and algorithms / Prediction and decision losses
Learning Optimal Decision Trees Using Caching Branch-and-Bound Search
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| DL8.5 (branch-and-bound + partial-result caching) instead of BinOCT MIP | optimal decision tree learning · 24 binary datasets, depth 4 | instances solved: 1 -> 19 | Helped |
| add branch-and-bound and partial caching to DL8 | DL8 · 24 binary datasets, depth 4 | instances solved: 7 -> 19 | Helped |
| cache incomplete (NO TREE) results (ablation) | DL8.5 · 24 binary datasets, depth 4 | instances solved: 12 -> 19 | Helped |
| order attributes by decreasing information gain vs other orders (ablation) | DL8.5 · binary datasets, deeper trees | runtime | Helped |
| branching heuristic choice (ablation) | DL8.5 · continuous datasets, depth 2 | runtime | No clear effect |
| DL8.5 instead of DL8 | optimal decision tree learning · banknote, depth 2 | time (s): 363.01 -> 52.81 | Helped |
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