notebooks/README.md

notebooks/

Training and evaluation notebooks for MLP and XGBoost models.

1. Files

Notebook	Model	Description
mlp.ipynb	PyTorch MLP	Training, evaluation, and LOPO cross-validation
xgboost.ipynb	XGBoost	Training, evaluation, and LOPO cross-validation

2. Structure (both notebooks)

Each notebook follows the same layout:

1. Imports and CFG — single config dict, project root setup
2. ClearML (optional) — opt-in experiment tracking
3. Data loading — uses data_preparation.prepare_dataset for consistent loading
4. Random split training — 70/15/15 stratified split with per-epoch/round logging
5. Loss curves — train vs validation loss plots
6. Test evaluation — accuracy, F1, ROC-AUC, classification report, confusion matrix
7. Checkpoint saving — model weights + JSON training log
8. LOPO evaluation — Leave-One-Person-Out cross-validation across all 9 participants
9. LOPO summary — per-person accuracy table + bar chart

3. Running

Open in Jupyter or VS Code with the Python kernel set to the project venv:

source venv/bin/activate
jupyter notebook notebooks/mlp.ipynb

Make sure the kernel's working directory is either the project root or notebooks/ — the path resolution handles both.

4. Results

Model	Random Split Accuracy	Random Split F1	LOPO (mean)
XGBoost	95.87%	0.959	see notebook
MLP	92.92%	0.929	see notebook