# Step 1: model-notebooks

## 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/`
- `/src/femsurrogate/plotting/`

## 작업

모델별 notebook 5개를 만든다.

- `notebooks/01_response_surface_surrogate.ipynb`
- `notebooks/02_gaussian_process_kriging_surrogate.ipynb`
- `notebooks/03_random_forest_surrogate.ipynb`
- `notebooks/04_gradient_boosting_surrogate.ipynb`
- `notebooks/05_mlp_surrogate.ipynb`

각 notebook은 같은 dataset, target, split seed를 쓰고 metric JSON과 prediction CSV를 저장한다.

## Acceptance Criteria

```powershell
uv run jupyter nbconvert --to notebook --execute notebooks/01_response_surface_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/02_gaussian_process_kriging_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/03_random_forest_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/04_gradient_boosting_surrogate.ipynb
uv run jupyter nbconvert --to notebook --execute notebooks/05_mlp_surrogate.ipynb
uv run ruff check .
```

## 금지사항

- 모델별로 다른 split을 쓰지 마라.