https://github.com/classifier-calibration/survey-old
How to assess and improve predicted class probabilities
Science Score: 36.0%
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Found 4 DOI reference(s) in README -
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Repository
How to assess and improve predicted class probabilities
Basic Info
- Host: GitHub
- Owner: classifier-calibration
- Default Branch: main
- Size: 5.86 KB
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- Stars: 0
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Metadata Files
README.md
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}
}
Owner
- Name: classifier-calibration
- Login: classifier-calibration
- Kind: organization
- Repositories: 5
- Profile: https://github.com/classifier-calibration