# Risk Control Project (mlrisko)
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[](https://codecov.io/gh/thibaultcordier/risk-control)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://badge.fury.io/py/mlrisko)
[](https://thibaultcordier.github.io/risk-control/)
[](https://github.com/astral-sh/ruff)
[](https://github.com/astral-sh/ruff)
[](https://mypy-lang.org/)
**mlrisko** (MLRiskControl) is a comprehensive toolkit for implementing risk control mechanisms for predictive algorithms based on the paper "Learn then test: Calibrating predictive algorithms to achieve risk control" by Angelopoulos et al. (2025).
The primary goal is to ensure that machine learning algorithms perform reliably and maintain a controlled level of risk through advanced calibration techniques.
## Installation
To install the necessary dependencies, run:
```bash
uv sync
uv pip install -e .
```
For development purposes, you can install the development dependencies with:
```bash
uv sync --all-groups
```
## Running the Example
To run the example, execute the following command:
```bash
uv run python examples/plot_regression.py
uv run python examples/plot_classification.py
uv run python examples/plot_classification_bis.py
```
## Documentation
For detailed documentation, refer to the [docs](https://thibaultcordier.github.io/risk-control/).
Or you can build the documentation with:
```bash
uv run mkdocs serve
```
## License
This project is licensed under the BSD 3-Clause License. See the [LICENSE](LICENSE) file for details.
## References
Angelopoulos, A. N., Bates, S., Candès, E. J., Jordan, M. I., & Lei, L. (2025). Learn then test: Calibrating predictive algorithms to achieve risk control. The Annals of Applied Statistics, 19(2), 1641-1662.
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"description": "# Risk Control Project (mlrisko)\n\n[](https://github.com/thibaultcordier/risk-control/actions)\n[](https://codecov.io/gh/thibaultcordier/risk-control)\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/BSD-3-Clause)\n[](https://badge.fury.io/py/mlrisko)\n[](https://thibaultcordier.github.io/risk-control/)\n[](https://github.com/astral-sh/ruff)\n[](https://github.com/astral-sh/ruff)\n[](https://mypy-lang.org/)\n\n**mlrisko** (MLRiskControl) is a comprehensive toolkit for implementing risk control mechanisms for predictive algorithms based on the paper \"Learn then test: Calibrating predictive algorithms to achieve risk control\" by Angelopoulos et al. (2025).\n\nThe primary goal is to ensure that machine learning algorithms perform reliably and maintain a controlled level of risk through advanced calibration techniques.\n\n## Installation\n\nTo install the necessary dependencies, run:\n\n```bash\nuv sync\nuv pip install -e .\n```\n\nFor development purposes, you can install the development dependencies with:\n```bash\nuv sync --all-groups\n```\n\n## Running the Example\n\nTo run the example, execute the following command:\n\n```bash\nuv run python examples/plot_regression.py\nuv run python examples/plot_classification.py\nuv run python examples/plot_classification_bis.py\n```\n\n## Documentation\n\nFor detailed documentation, refer to the [docs](https://thibaultcordier.github.io/risk-control/).\n\nOr you can build the documentation with:\n```bash\nuv run mkdocs serve\n```\n\n## License\n\nThis project is licensed under the BSD 3-Clause License. See the [LICENSE](LICENSE) file for details.\n\n## References\n\nAngelopoulos, A. N., Bates, S., Cand\u00e8s, E. J., Jordan, M. I., & Lei, L. (2025). Learn then test: Calibrating predictive algorithms to achieve risk control. The Annals of Applied Statistics, 19(2), 1641-1662.\n",
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