Classification on australian (Average Accuracy & Std Dev)
89.49Average AccuracyAdaBoost
Evaluation results
| Method | Links | ||
|---|---|---|---|
| AdaBoostEnsemble size (L)=50, Evaluation protocol=10 iterations of 5-fold cross-validation, Tree setting=unpruned2026.06 | 89.49 | 0.0076 | |
| Random ForestEnsemble size (L)=50, Evaluation protocol=10 iterations of 5-fold cross-validation, Tree setting=unpruned2026.06 | 89.31 | 0.0088 | |
| BaggingEnsemble size (L)=50, Evaluation protocol=10 iterations of 5-fold cross-validation, Tree setting=unpruned2026.06 | 88.02 | 0.0065 | |
| CLDA ForestEnsemble size (L)=50, Evaluation protocol=10 iterations of 5-fold cross-validation2026.06 | 87.68 | 0.0036 | |
| RF2025.03 | 87.5 | 2.5 | |
| Rotation ForestEnsemble size (L)=50, Evaluation protocol=10 iterations of 5-fold cross-validation, Number of subsets (K)=optimal (1-4)2026.06 | 87.04 | 0.0062 | |
| OCT2025.03 | 85 | 1.7 | |
| OCMT-H2025.03 | 84.3 | 1.6 | |
| LMT2025.03 | 84.3 | 2.9 | |
| LS-OMS2025.03 | 84.3 | 2.5 | |
| CART2025.03 | 84.2 | 2.9 | |
| SVM2025.03 | 84.2 | 2.5 | |
| DL8.52025.03 | 84.2 | 3.5 | |
| OCMT2025.03 | 83.5 | 1.8 | |
| OCT-H2025.03 | 81.8 | 1.9 | |
| KFDA ForestEnsemble size (L)=50, Evaluation protocol=10 iterations of 5-fold cross-validation, Number of subsets (K)=optimal (1-4)2026.06 | 79.4 | 0.0141 |