Research map / Learning theory and algorithms / Prediction and decision losses
Towards Better Decision Forests: Forest Alternating Optimization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · MNIST | 0-1 error (%): 1.62±0.00 -> 1.33±0.04 | Helped |
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · news20 | 0-1 error (%): 16.65±0.04 -> 16.59±0.13 | Helped |
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · CIFAR100 | 0-1 error (%): 26.71±0.05 -> 26.55±0.12 | Helped |
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · SUSY | 0-1 error (%): 19.5748 -> 19.4994 | Helped |
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · cpuact | RMSE: 2.25±0.00 -> 2.18±0.00 | Helped |
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · CT-slice | RMSE: 4.47±0.24 -> 4.37±0.18 | Helped |
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · casp | RMSE: 3.39±0.01 -> 3.37±0.01 | Helped |
| Use averaged FAO forests instead of the strongest reported baseline | Decision forest · year | RMSE: 8.73±0.01 -> 8.70±0.00 | Helped |
| Average independently initialized FAO forests (ablation) | FAO forest · Generalization test sets | test error | Helped |
| Optimize a single FAO forest rather than averaging forests (ablation) | FAO forest · cpuact and illustrative regression | test error | Hurt |
| Increase the number of FAO optimization iterations (ablation) | Averaged FAO forest · MNIST and cpuact | test error | Helped |
| Use very few trees in each FAO forest (ablation) | Averaged FAO forest · MNIST and cpuact | test error | Hurt |
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