# evaluation/

Training logs, threshold/weight analysis, metrics. **LOPO** (9 folds) + **Youden’s J** + weight grid search — see `justify_thresholds.py`.

**Contents:** `logs/` (JSON from training runs), `plots/` (ROC, weight search, EAR/MAR), `justify_thresholds.py`, `feature_importance.py`, and the generated markdown reports.

**Logs:** MLP writes `face_orientation_training_log.json`, XGBoost writes `xgboost_face_orientation_training_log.json`. Paths: `evaluation/logs/`.

**Threshold report:** Generate `THRESHOLD_JUSTIFICATION.md` and plots with:

```bash
python -m evaluation.justify_thresholds
```

(LOPO over 9 participants, Youden’s J, weight grid search; ~10–15 min.) Outputs go to `plots/` and the markdown file.

**Feature importance:** Run `python -m evaluation.feature_importance` for full XGBoost gain + leave-one-feature-out LOPO (slow).  
Fast iteration mode: `python -m evaluation.feature_importance --quick --skip-lofo` (channel ablation + gain only).

**Grouped benchmark:** Run `python -m evaluation.grouped_split_benchmark` for full run, or `python -m evaluation.grouped_split_benchmark --quick` for faster approximate numbers.

**Who writes here:** `models.mlp.train`, `models.xgboost.train`, `evaluation.justify_thresholds`, `evaluation.feature_importance`, and the notebooks.