oracle/macest
Model Agnostic Confidence Estimator (MACEST) - A Python library for calibrating Machine Learning models' confidence scores
When you rely on machine learning for critical decisions, MACEst helps you understand how trustworthy each individual prediction is. It takes your existing model's predictions and data, then outputs a confidence score for classification tasks or a confidence interval for regression tasks. This is for data scientists, machine learning engineers, and analysts working with supervised learning models in high-stakes fields.
100 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to know how confident your machine learning model is about each specific prediction, especially when wrong predictions can have significant consequences.
Not ideal if you only care about overall model accuracy and don't need to assess the certainty of individual predictions or understand when your model is operating outside its known data.
Stars
100
Forks
18
Language
Jupyter Notebook
License
UPL-1.0
Last pushed
Sep 26, 2025
Commits (30d)
0
Dependencies
8
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