CalibrationErrors.jl

Estimation of calibration errors.

A package for estimating calibration errors from data sets of predictions and targets.

CalibrationTests.jl implements statistical hypothesis tests of calibration.

pycalibration is a Python interface for CalibrationErrors.jl and CalibrationTests.jl.

rcalibration is an R interface for CalibrationErrors.jl and CalibrationTests.jl.

Talk at JuliaCon 2021

The slides of the talk are available as Pluto notebook.

Citing

If you use CalibrationErrors.jl as part of your research, teaching, or other activities, please consider citing the following publications:

Widmann, D., Lindsten, F., & Zachariah, D. (2019). Calibration tests in multi-class classification: A unifying framework. In Advances in Neural Information Processing Systems 32 (NeurIPS 2019) (pp. 12257–12267).

Widmann, D., Lindsten, F., & Zachariah, D. (2021). Calibration tests beyond classification. International Conference on Learning Representations (ICLR 2021).

Acknowledgements

This work was financially supported by the Swedish Research Council via the projects Learning of Large-Scale Probabilistic Dynamical Models (contract number: 2016-04278), Counterfactual Prediction Methods for Heterogeneous Populations (contract number: 2018-05040), and Handling Uncertainty in Machine Learning Systems (contract number: 2020-04122), by the Swedish Foundation for Strategic Research via the project Probabilistic Modeling and Inference for Machine Learning (contract number: ICA16-0015), by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, and by ELLIIT.