Quad Model Ensemble¶
Simple 4-model voting ensemble for balanced performance and complexity.
Performance¶
| Metric | Value | Rank |
|---|---|---|
| ROC AUC | 0.6756 | 5th |
| F1 Score | 0.3500 | 12th |
| Accuracy | 0.7426 | 5th |
| Recall | 0.2333 | 13th |
| Train Time | 383s | Medium |
Architecture¶
Components¶
| Model | Weight | Purpose |
|---|---|---|
| GradientBoostingClassifier | 25% | Sequential boosting |
| RandomForestClassifier | 25% | Bagging, diversity |
| ExtraTreesClassifier | 25% | Random splits, speed |
| LogisticRegression | 25% | Linear baseline |
When to Use¶
Good Alternative
When meta_stacking is too slow but you want ensemble benefits:
- 15x faster than meta_stacking (383s vs 32,030s)
- Simpler to understand and debug
- Still captures ensemble diversity