Research map / Learning theory and algorithms / Prediction and decision losses
Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Jointly optimize the action vector and feature order instead of choosing the order after optimizing the action | OrdCE versus Greedy · FICO, German, WineQuality, and Diabetes | C_OrdCE: FICO, MLP, TLPS: 3.35 ± 3.0 -> 1.57 ± 1.4 | Helped |
| Jointly optimize the action vector and feature order instead of choosing the order after optimizing the action | OrdCE versus Greedy · FICO, German, WineQuality, and Diabetes | C_ord: FICO, MLP, TLPS: 3.05 ± 2.8 -> 0.84 ± 0.74 | Helped |
| Jointly optimize the action vector and feature order | OrdCE versus Greedy · Diabetes, RF, DACE | C_dist: 0.716 -> 0.716 | No clear effect |
| Jointly optimize the action vector and feature order | OrdCE versus Greedy · Diabetes, RF, DACE | C_ord: 0.825 -> 0.528 | Helped |
| Use exact MILO optimization rather than the Greedy procedure | OrdCE · FICO, MLP, TLPS | computation time (seconds): 12.6 ± 48 -> 183 ± 140 | Hurt |
| Increase the trade-off parameter γ | OrdCE · German and WineQuality | C_dist | Mixed |
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