CalibrationErrors.jl

Estimation of calibration errors.
Author devmotion
Popularity
3 Stars
Updated Last
4 Months Ago
Started In
May 2019

CalibrationErrors.jl

Estimation of calibration errors.

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There are also Python and R interfaces for this package

Overview

This package implements different estimators of the expected calibration error (ECE), the squared kernel calibration error (SKCE), and the unnormalized calibration mean embedding (UCME) in the Julia language.

Example

Calibration errors can be estimated from a data set of predicted probability distributions and a set of corresponding observed targets by executing

estimator(predictions, targets)

The sets of predictions and targets have to be provided as vectors.

This package implements the estimator ECE of the ECE, the estimators BiasedSKCE, UnbiasedSKCE, and BlockUnbiasedSKCE for the SKCE, and UCME for the UCME.

Related packages

CalibrationErrorsDistributions.jl extends calibration error estimation in this package to more general probabilistic predictive models that output arbitrary probability distributions.

CalibrationTests.jl implements statistical hypothesis tests of calibration.

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

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

References

If you use CalibrationsErrors.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. To be presented at ICLR 2021.