Research map / Learning theory and algorithms / Prediction and decision losses
Random Composite Forests
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Random Composite Forest instead of Random Forests | RCF · UCI vowel | test error (%): 5.28 ± 0.46 -> 3.77 ± 0.45 | Helped |
| use Random Composite Forest instead of Random Forests | RCF · UCI pendigits | test error (%): 1.25 ± 0.14 -> 0.29 ± 0.03 | Helped |
| use Random Composite Forest instead of Random Forests | RCF · UCI usps | test error (%): 9.80 ± 0.30 -> 7.48 ± 0.16 | Helped |
| use Random Composite Forest instead of Random Forests | RCF · UCI sonar | test error (%): 21.9 ± 4.85 -> 16.2 ± 3.80 | Helped |
| use Random Composite Forest instead of Random Forests | RCF · UCI vehicle | test error (%): 28.4 ± 0.53 -> 27.9 ± 1.10 | No clear effect |
| use Random Composite Forest instead of Random Forests | RCF · UCI a2a | test error (%): 18.1 ± 0.49 -> 17.5 ± 0.18 | Helped |
| use Random Composite Forest instead of Random Forests | RCF · UCI abalone | test error (%): 75.3 ± 1.15 -> 71.7 ± 0.66 | Helped |
| use Random Composite Forest instead of Random Forests | RCF · UCI iris | test error (%): 13.3 ± 1.10 -> 10.0 ± 1.50 | Helped |
| select leaf classifiers with the generalization bound instead of plain SVM leaves (RF-SVM) (ablation) | RCF · UCI dna | test error (%): 6.3 ± 1.79 -> 3.30 ± 0.10 | Helped |
| select leaf classifiers with the generalization bound instead of plain SVM leaves (RF-SVM) (ablation) | RCF · UCI sonar | test error (%): 28.09 ± 2.3 -> 16.2 ± 3.80 | Helped |
| select leaf classifiers with the generalization bound instead of plain SVM leaves (RF-SVM) (ablation) | RCF · UCI vehicle | test error (%): 31.2 ± 2.19 -> 27.9 ± 1.10 | Helped |
| select leaf classifiers with the generalization bound instead of plain SVM leaves (RF-SVM) (ablation) | RCF · UCI german | test error (%): 24.2 ± 0.60 -> 23.0 ± 0.63 | Hurt |
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