evaluation/feature_selection_justification.md

# Feature selection justification

The face_orientation model uses 10 of 17 extracted features. This document summarises empirical support.

## 1. Domain rationale

The 10 features were chosen to cover three channels:
- **Head pose:** head_deviation, s_face, pitch
- **Eye state:** ear_left, ear_right, ear_avg, perclos
- **Gaze:** h_gaze, gaze_offset, s_eye

Excluded: v_gaze (noisy), mar (rare events), yaw/roll (redundant with head_deviation/s_face), blink_rate/closure_duration/yawn_duration (temporal overlap with perclos).

## 2. XGBoost feature importance (gain)

Config used: `{'n_estimators': 600, 'max_depth': 8, 'learning_rate': 0.1489, 'subsample': 0.9625, 'colsample_bytree': 0.9013, 'reg_alpha': 1.1407, 'reg_lambda': 2.4181, 'eval_metric': 'logloss'}`.
Quick mode: yes (200 trees)

From the trained XGBoost checkpoint (gain on the 10 features):

| Feature | Gain |
|---------|------|
| head_deviation | 8.83 |
| s_face | 10.27 |
| s_eye | 2.18 |
| h_gaze | 4.99 |
| pitch | 4.64 |
| ear_left | 3.57 |
| ear_avg | 6.96 |
| ear_right | 9.54 |
| gaze_offset | 1.80 |
| perclos | 5.68 |

**Top 5 by gain:** s_face, ear_right, head_deviation, ear_avg, perclos.

## 3. Leave-one-feature-out ablation (LOPO)

Baseline (all 10 features) mean LOPO F1: **0.8286**.

Skipped in this run (`--skip-lofo`).

## 4. Channel ablation (LOPO)

| Subset | Mean LOPO F1 |
|--------|--------------|
| head_pose | 0.7480 |
| eye_state | 0.8071 |
| gaze | 0.7260 |
| all_10 | 0.8286 |

## 5. Conclusion

Selection is supported by (1) domain rationale (three attention channels), (2) XGBoost gain importance, and (3) channel ablation. Run without `--skip-lofo` for full leave-one-out ablation.