Research map / Learning theory and algorithms / Prediction and decision losses
Ranking Data with Continuous Labels through Oriented Recursive Partitions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use continuous-ranking optimization rather than CART's MSE-minimizing regression objective | Decision-tree learning on the toy regression example · Toy regression example | IAUC: 0.61 -> 0.95 | Helped |
| Use continuous-ranking optimization rather than CART's MSE-minimizing regression objective | Decision-tree learning on the toy regression example · Toy regression example | Kendall τ: 0.58 -> 0.92 | Helped |
| Use continuous-ranking optimization rather than CART's MSE-minimizing regression objective | Decision-tree learning on the toy regression example · Toy regression example | MSE: 7.4 \times 10^{-4} -> 0.10 | Hurt |
| Maximize empirical Kendall τ instead of empirical IAUC (ablation) | CRank · Toy regression example | IAUC: 0.95 -> 0.94 | Hurt |
| Maximize empirical Kendall τ instead of empirical IAUC (ablation) | CRank · Toy regression example | Kendall τ: 0.92 -> 0.93 | Helped |
| Maximize empirical Kendall τ instead of empirical IAUC (ablation) | CRank · Toy regression example | MSE: 0.10 -> 0.10 | No clear effect |
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