# 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)

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: **0.8327**.

| Feature dropped | Mean LOPO F1 | Δ vs baseline |
|------------------|--------------|---------------|
| head_deviation | 0.8395 | -0.0068 |
| s_face | 0.8390 | -0.0063 |
| s_eye | 0.8342 | -0.0015 |
| h_gaze | 0.8244 | +0.0083 |
| pitch | 0.8250 | +0.0077 |
| ear_left | 0.8326 | +0.0001 |
| ear_avg | 0.8350 | -0.0023 |
| ear_right | 0.8344 | -0.0017 |
| gaze_offset | 0.8351 | -0.0024 |
| perclos | 0.8258 | +0.0069 |

Dropping **h_gaze** hurts most (F1=0.8244), consistent with it being important.

## 4. Conclusion

Selection is supported by (1) domain rationale (three attention channels), (2) XGBoost gain importance, and (3) leave-one-out ablation. SHAP or correlation-based pruning can be added in future work.