# Step 2: surrogate-models

## 읽어야 할 파일

- `/AGENTS.md`
- `/docs/theory/02_response_surface_methodology.md`
- `/docs/theory/03_gaussian_process_kriging.md`
- `/docs/theory/04_random_forest.md`
- `/docs/theory/05_gradient_boosting.md`
- `/docs/theory/06_mlp_neural_network.md`
- `/src/femsurrogate/surrogates/common.py`

## 작업

TDD로 모델별 scikit-learn pipeline builder와 registry를 구현한다.

- 테스트: `/tests/test_surrogate_models.py`.
- 구현: `rsm.py`, `gpr.py`, `random_forest.py`, `boosting.py`, `mlp.py`, `registry.py`.
- 모델명: `rsm`, `gpr`, `random_forest`, `gradient_boosting`, `mlp`.

## Acceptance Criteria

```powershell
uv run pytest tests/test_surrogate_models.py -q
uv run ruff check .
```

## 금지사항

- PyTorch/TensorFlow, AutoML, MLflow를 추가하지 마라.