Research map / Learning theory and algorithms / Combinatorial and search optimization
A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use SVM1-ODT MIP instead of CART | depth-2 decision tree · UCI medium datasets | test accuracy: 92.4 -> 98.6 (Iris) | Helped |
| use SVM1-ODT instead of OCT-H | depth-2 decision tree · UCI medium datasets | test accuracy: 94.4 -> 98.6 (Iris) | Helped |
| use SVM1-ODT instead of Fair MIP | depth-2 decision tree · Hayes-roth | test accuracy: 72.2 vs 71.9 | No clear effect |
| use depth 3 SVM1-ODT instead of CART | multi-class (>4) datasets · Dermatology | test accuracy: 76.1 -> 98.9 | Helped |
| use SVM1-ODT instead of Fair | depth-3 tree · Heart-disease | test accuracy: 47.2 -> 65.3 | Helped |
| add LP-based data selection to SVM1-ODT (ablation) | SVM1-ODT · large datasets (7k-245k samples) | test accuracy | Helped |
| train plain MIP (OCT-H/S1O) on large data without data selection (ablation) | MIP ODT · large datasets | test accuracy | No clear effect |
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