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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.
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