🧠 Key Insights
- Tree-based models (RF, XGBoost) fail under extreme imbalance, predicting only the majority class.
- Linear models achieve high recall but suffer from extremely low precision.
- Threshold tuning significantly improves performance:
- F1 improved from 0.0085 → 0.0769 (LogReg)
⚙️ Usage
import joblib
model = joblib.load("models/logistic_regression.joblib")
preds = model.predict(X)
⚠️ Limitations
Models struggle with extreme imbalance (~1600:1) Poor generalization across subjects (LOSO results) Classical ML is insufficient for robust seizure detection in this setting
📚 Citation
If you use this model, please cite:
@dataset{eegparquet_benchmark_2026,
title={EEGParquet-Benchmark: Windowed and Feature-Enriched EEG Dataset for Seizure Detection},
author={Daffa Tarigan},
year={2026},
publisher={Hugging Face}
}
🚀 Notes
This repository is intended for:
Benchmarking classical ML under imbalance Demonstrating limitations of accuracy-based evaluation Supporting research in biomedical signal classification
1. Folder structure (important)
/models
├── logistic_regression.joblib
├── random_forest.joblib
├── svm_rbf_cuml_gpu.joblib
├── xgboost_gpu_optuna.joblib
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from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("LeBabyOx/EEGDetectionMLBaseline", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html