fklearn uses functional programming principles to make it easier to solve real problems with Machine Learning.
The name is a reference to the widely known scikit-learn library.
fklearn Principles
- Validation should reflect real-life situations.
- Production models should match validated models.
- Models should be production-ready with few extra steps.
- Reproducibility and in-depth analysis of model results should be easy to achieve.
Documentation | Getting Started | API Docs | Contributing |
To install via pip:
pip install fklearn
To install with optional dependencies:
pip install fklearn[lgbm] # LightGBM support
pip install fklearn[xgboost] # XGBoost support
pip install fklearn[catboost] # CatBoost support
pip install fklearn[all_models] # All model backends
pip install fklearn[all] # All models + tools
uv sync --extra develuv run pytest --cov=src/uv run ruff check src/ tests/
uv run ruff format src/ tests/uv add <package-name> # runtime dependency
uv add --dev <package-name> # dev dependency