calzone is a comprehensive Python package for calculating and visualizing metrics for assessing the calibration of models with probabilistic output.
- Supports multiple calibration metrics including Spiegelhalter's Z-test, Expected Calibration Error (ECE), Maximum Calibration Error (MCE), Hosmer-Lemeshow (HL) test, Cox regression analysis, and Loess regression analysis.
- Provides tools for creating reliability diagrams and ROC curves.
- Offers equal-space and equal-frequency binning options.
- Provides bootstrapped confidence intervals for each calibration metric.
- Supports prevelance adjustment to account for prevalance differences between enriched data and population data.
- Extends metrics to multiclass classification problems with one-vs-rest or top-class calculations.
To accurately assess the calibration of machine learning models, it is essential to have a comprehensive and representative dataset with sufficient coverage of the prediction space. The calibration metrics are not meaningful if the dataset is not representative of true intended population.
You can install calzone using pip:
pip install calzone-tool
run python cal_metrics.py -h to see the help information and usage. To use the package in your Python code, please refer to the examples in the documentation pages.
A GUI is available by running python GUI_cal_metrics.py. Support for the GUI is experiment and requires additional dependencies (i.e., nicegui).
For a detailed manual and API reference, please visit our documentation page.
If you encounter any issues or have questions about the package, please open an issue request or contact:
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