Classifier Calibration

A survey on how to assess and improve predicted class probabilities

Note

This content is deprecated and has been moved to the repository classifier-calibration.git

Peter Flach, University of Bristol, UK, Peter.Flach@bristol.ac.uk , www.cs.bris.ac.uk/~flach/

Miquel Perello-Nieto, University of Bristol, UK, miquel.perellonieto@bristol.ac.uk, https://www.perellonieto.com/

Hao Song, University of Bristol, UK, hao.song@bristol.ac.uk

Meelis Kull, University of Tartu, Estonia, meelis.kull@ut.ee

Telmo Silva Filho, Federal University of Paraiba, Brazil, telmo@de.ufpb.br

Tools

We are developing a Python library with tools to evaluate the calibration of models. PyCalib has its own documentation page, and can be installed from the Python Package Index Pypi pip install pycalib.

Citation

This work has been published in the Machine Learning journal. You may want to use the following citation if you want to reference this work.

@Article{SilvaFilho2023,
author={Silva Filho, Telmo
and Song, Hao
and Perello-Nieto, Miquel
and Santos-Rodriguez, Raul
and Kull, Meelis
and Flach, Peter},
title={Classifier calibration: a survey on how to assess and improve predicted class probabilities},
journal={Machine Learning},
year={2023},
month={May},
day={16},
issn={1573-0565},
doi={10.1007/s10994-023-06336-7},
url={https://doi.org/10.1007/s10994-023-06336-7}
}