# evaluation/

Training logs, threshold/weight analysis, and metrics.

**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 XGBoost gain and leave-one-feature-out LOPO; writes `feature_selection_justification.md`.

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