https://github.com/valeman/calibration_is_what_you_need
https://github.com/valeman/calibration_is_what_you_need
Last synced: 5 months ago
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- Host: GitHub
- URL: https://github.com/valeman/calibration_is_what_you_need
- Owner: valeman
- Created: 2024-08-28T13:03:52.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-02T16:38:41.000Z (over 1 year ago)
- Last Synced: 2025-03-02T17:32:14.247Z (over 1 year ago)
- Size: 14.6 KB
- Stars: 24
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# calibration_is_what_you_need
## [Table of Contents]()
* [Papers](#papers)
* [Articles](#articles)
* [Kaggle](#kaggle)
* [Tutorials](#tutorials)
* [Courses](#courses)
* [Presentation_slides](#presentation-slides)
* [Researchers](#researchers)
* [Websites](#websites)
* [Twitter](#twitter)
* [TikTok](#tiktok)
* [Conferences](#conferences)
* [Python](#python)
* [R](#r)
## Theses
1. [An Evaluation of Calibration Methods for Data Mining Models in Simulation Problems](https://riunet.upv.es/bitstream/handle/10251/13631/Antonio%20Bella%20-%20Master%20Thesis.pdf?sequence=1&isAllowed=y)
## Papers
1. [Properties and Benefits of Calibrated Classifiers](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=acf9bcef4d3b436041d353c11b8c16cfe4e3087a) by Ira Cohen & Moises Goldszmidt (2004)
2. [An Empirical Comparison of Supervised Learning Algorithms](https://www.cs.cornell.edu/~caruana/ctp/ct.papers/caruana.icml06.pdf) by Rich Caruana and Alexandru Niculescu-Mizil (2005)
3. [On Calibration of Modern Neural Networks](https://arxiv.org/pdf/2303.10761)
4. [A Conformal Prediction Approach to Enhance Predictive Accuracy and Confidence in Machine Learning Application in Chronic Diseases](https://pubmed.ncbi.nlm.nih.gov/39176913/)
5. [Are Traditional Neural Networks Well-Calibrated?](https://ieeexplore.ieee.org/document/8851962/)
6. [Conformal prediction sets for ordinal classification](https://www.amazon.science/publications/conformal-prediction-sets-for-ordinal-classification)
7. [Regression Compatible Listwise Objectives for Calibrated Ranking with Binary Relevance](https://arxiv.org/abs/2211.01494) Google, 2023
8. [LiRank: Industrial Large Scale Ranking Models at LinkedIn](https://arxiv.org/abs/2402.06859) (2024)
9. [AUC: a misleading measure of the performance of predictive distribution models](https://www2.unil.ch/biomapper/Download/Lobo-GloEcoBioGeo-2007.pdf) (2007)
10. [Towards a Rigorous Calibration Assessment Framework: Advancements in Metrics, Methods](https://ebooks.iospress.nl/doi/10.3233/FAIA230327) by Famiglini, Andrea Campagner, Federico Cabitza (Universiteta degli Studi di Milano-Bicocca, Milan, Italy; Istituto Ortopedico Galeazzi, Milan, Italy, 2023) [code](https://github.com/lorenzofamiglini/CalFram)
11. [Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification](https://arxiv.org/abs/2102.07856) (2021)
## Articles
1. [Calibrating Classifiers in Reality](https://abnormalsecurity.com/blog/calibrating-classifiers-in-reality)
## Video
1. [PS3:Calibrated recommendations Harold Steck](https://www.youtube.com/watch?v=AlsPvceHVj4)
## Python
1. [PyCalEva - A framework for calibration evaluation of binary classification models](https://github.com/MartinWeigl/pycaleva)
1. [Calibration of Probability Outputs for Classifiers](https://github.com/DIDSR/calzone)
2. [scores: Metrics for the verification, evaluation and optimisation of forecasts, predictions or models](https://github.com/nci/scores) [paper](https://joss.theoj.org/papers/10.21105/joss.06889)
3. [reliability_diagram](https://github.com/hollance/reliability-diagrams)
4. [classifier-calibration](https://github.com/zygmuntz/classifier-calibration)
5. [PyCalib](https://classifier-calibration.github.io/PyCalib/)
6. [Library for the Test-based Calibration Error (TCE) metric to quantify the degree to classifier calibration.](https://github.com/facebookresearch/tce)
7. [Website for the Classifier calibration tutorial, ECML-PKDD 2020](https://github.com/classifier-calibration/classifier-calibration.github.io)
8. [net:cal - Uncertainty Calibration](https://github.com/EFS-OpenSource/calibration-framework)
9. [relplot: Principled Reliability Diagrams](https://github.com/apple/ml-calibration)