aangelopoulos/conformal_classification
Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
This project helps scientists, researchers, and engineers working with machine learning models by adding a crucial layer of certainty to their classifications. Instead of a single, potentially incorrect prediction, you get a 'prediction set' – a small group of possible classes that is guaranteed to include the true answer a high percentage of the time. This is invaluable when high confidence and error control are critical for image analysis, medical diagnostics, or risk assessment.
255 stars. No commits in the last 6 months.
Use this if you need provable guarantees that your image classifier's predictions will contain the correct answer with a high, user-defined probability, making your model's outputs more reliable and trustworthy.
Not ideal if you simply need the single most likely prediction from your model and do not require formal probabilistic guarantees or prediction sets.
Stars
255
Forks
36
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 23, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aangelopoulos/conformal_classification"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
valeman/awesome-conformal-prediction
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers,...
zillow/quantile-forest
Quantile Regression Forests compatible with scikit-learn.
yromano/cqr
Conformalized Quantile Regression
henrikbostrom/crepes
Python package for conformal prediction
xRiskLab/pearsonify
Lightweight Python package for generating classification intervals in binary classification...