Skip to content

Estimating Expected Calibration Errors

Uncertainty in probabilistic classifiers predictions is a key concern when models are used to support human decision making, in broader probabilistic pipelines or when sensitive automatic decisions have to be taken.
Studies have shown that most models are not intrinsically well calibrated, meaning that their decision scores are not consistent with posterior probabilities.
Hence being able to calibrate these models, or enforce calibration while learning them, has regained interest in recent literature.
In this context, properly assessing calibration is paramount to quantify new contributions tackling calibration.
However, there is room for improvement for commonly used metrics and evaluation of calibration could benefit from deeper analyses.
Thus this paper focuses on the empirical evaluation of calibration metrics in the context of classification.
More specifically it evaluates different estimators of the Expected Calibration Error ($ECE$), amongst which legacy estimators and some novel ones, proposed in this paper.
We build an empirical procedure to quantify the quality of these $ECE$ estimators, and use it to decide which estimator should be used in practice for different settings.

Nicolas Posocco, Antoine Bonnefoy, Estimating Expected Calibration Errors, In Proc. of the The 30th International Conference on Artificial Neural Networks, 2021.

Watch the presentation on YouTube.

Click here to access the paper.

Share on linkedin
Share on twitter
Share on email

Releated Posts

Multimodal Classifier For Space Target Recognition

We propose a multi-modal framework to tackle the SPARK Challenge by classifying satellites using RGB and depth images. Our framework is mainly based on Auto-Encoders to embed the two modalities in a common latent space in order to exploit redundant and complementary information between the two types of data.
Read More

Advancing Innovation in Data Management

To build a data-driven economy across Europe and create a significant competitive advantage for European industry, companies will have to address the challenges in the data engineering and management domain. We are proud to partner with four top-class European institutions.
Read More