Classification on Cryopathy diagnostic dataset (test)
38.09AccuracyR5: RF + Aggressive Aug
Evaluation results
| Method | Links | ||||
|---|---|---|---|---|---|
| R5: RF + Aggressive AugNotes=SMOTE 100% + noise2026.06 | 38.09 | 13.01 | 27.46 | 16.93 | |
| R1: GBT Tuned + SMOTENotes={'ni': 35, 'md': 4, 'lr': 0.15, 'ml': 8}2026.06 | 33.77 | 14.96 | 27.02 | 16.54 | |
| R4: Period-Aware ModelsNotes=533/5332026.06 | 27.2 | 16.7 | 24.87 | 12.44 | |
| Ensemble: RF+GBT (equal)Notes=Soft voting, 50/502026.06 | 26.64 | 17.04 | 25.2 | 13.11 | |
| Ensemble: RF+GBT (weighted)Notes=Soft voting, 40/602026.06 | 26.64 | 16.34 | 25.01 | 13.03 | |
| Baseline RF + SMOTENotes=50 trees, SMOTE 70%2026.06 | 25.33 | 16.85 | 24.17 | 11.98 | |
| Triple Ensemble (RF+GBT+MLP)Notes=3-model soft voting2026.06 | 25.14 | 15.96 | 23.72 | 12.2 | |
| R2: Hierarchical (type to diagnosis)Notes=L1 acc=0.3862026.06 | 24.95 | 14.82 | 23.83 | 10.64 | |
| MLP: MLP(64-32)Notes=2.6s2026.06 | 14.82 | 10.15 | 15.07 | 4.79 | |
| MLP: MLP(128-64,reg+)Notes=3.5s2026.06 | 14.26 | 11.22 | 13.68 | 6.31 | |
| MLP: MLP(128-64)Notes=4.6s2026.06 | 13.51 | 10.65 | 12.68 | 5.44 |