mit-ll-responsible-ai/equine
Establishing Quantified Uncertainty in Neural Networks
When working with machine learning models that categorize or label data, it's crucial to understand not just what the model predicts, but also how confident it is and if the data even fits within what it was trained on. This tool takes your existing deep neural network and gives you enhanced predictions, including calibrated probabilities for each label and a score indicating if the input data is truly similar to the data the model learned from. Data scientists and machine learning engineers who need to build more trustworthy and transparent AI systems will find this invaluable.
Available on PyPI.
Use this if you need to understand the reliability of your deep neural network's predictions and ensure it doesn't make overconfident guesses or predictions on unfamiliar data.
Not ideal if your primary goal is to train a new model from scratch or if you are not working with supervised labeling tasks.
Stars
15
Forks
1
Language
Python
License
MIT
Last pushed
Jan 14, 2026
Commits (30d)
0
Dependencies
7
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