vacquaviva/IntroConformalPredictions
A short introduction to Conformal Prediction methods, with a few examples for classification and regression from the Astrophysical domain, and slides.
This project helps astronomers and astrophysicists understand the inherent uncertainty in their predictive models. It takes in astronomical data used for classification (e.g., identifying galaxy types) or regression (e.g., predicting star properties) and provides a range of probable outcomes rather than a single point estimate. This allows researchers to quantify the reliability of their predictions.
No commits in the last 6 months.
Use this if you are an astronomer or astrophysicist who needs to robustly quantify the uncertainty of your machine learning predictions when classifying astronomical objects or regressing astrophysical properties.
Not ideal if you are looking for a general-purpose, production-ready machine learning library for domains outside of astronomy, or if you only need point predictions without uncertainty quantification.
Stars
13
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
Jul 02, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/vacquaviva/IntroConformalPredictions"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
zillow/quantile-forest
Quantile Regression Forests compatible with scikit-learn.
valeman/awesome-conformal-prediction
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers,...
yromano/cqr
Conformalized Quantile Regression
henrikbostrom/crepes
Python package for conformal prediction
xRiskLab/pearsonify
Lightweight Python package for generating classification intervals in binary classification...