Research map / Learning theory and algorithms / Prediction and decision losses
Trading Complexity for Sparsity in Random Forest Explanations
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use majoritary reasons instead of sufficient reasons | Random forest explanations · placement and Gisette | explanation size | Helped |
| Use minimal majoritary reasons instead of sufficient reasons | Random forest explanations · placement | explanation size | Helped |
| Use minimal majoritary reasons instead of sufficient reasons | Random forest explanations · Gisette | explanation size | Helped |
| Use the anytime Partial MaxSAT algorithm to approximate minimal majoritary reasons | Random forest explanations · Gisette | explanation size | Helped |
| Increase the anytime Partial MaxSAT time limit | Minimal majoritary reason approximation · Gisette | explanation size | Helped |
| Compute minimal sufficient reasons | Random forest explanations · All tested datasets | instances completed within the time limit: 10 instances | Hurt |
| Use LIME explanations instead of direct forest explanations | Random forest explanations · placement and Gisette | explanation size | Mixed |
| Use LIME explanations instead of other explanation methods | Random forest explanations · placement | computation time | Hurt |
| Generate majoritary reasons with the greedy algorithm | Random forest explanations · Random forest instances | computation time | Helped |
| Use minimal majoritary reasons rather than greedy majoritary reasons | Random forest explanations · Gisette | completion within the time limit: 600s | Hurt |
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