lorenzofamiglini/CalFram
Calibration Framework for Machine Learning and Deep Learning
This framework helps you thoroughly understand how trustworthy your machine learning classification models are. You provide your model's predictions, the actual outcomes, and the predicted probabilities for each class. In return, you get a detailed assessment of your model's calibration, showing where it might be overconfident or underconfident. Data scientists, machine learning engineers, and researchers can use this to build more reliable models.
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Use this if you need to go beyond simple accuracy metrics and deeply understand whether your classification model's predicted probabilities truly reflect the likelihood of an event.
Not ideal if you are working with regression models or don't need detailed insights into model confidence for your classification tasks.
Stars
16
Forks
3
Language
Python
License
MIT
Category
Last pushed
Jul 18, 2025
Commits (30d)
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