seedatnabeel/SSCP
SSCP: Improving Adaptive Conformal Prediction Using Self-supervised Learning (AISTATS 2023)
This project helps machine learning practitioners improve the reliability of their predictions without needing more labeled data. It takes your existing labeled and unlabeled data, along with a base prediction model, and outputs more accurate prediction intervals. This is especially useful for data scientists and ML engineers building models for high-stakes applications.
No commits in the last 6 months.
Use this if you need to ensure your machine learning models provide reliable prediction intervals and adapt well to new, unseen data, particularly when labeled data is scarce.
Not ideal if you are looking for a general-purpose machine learning model training framework or if your primary goal is to improve point prediction accuracy rather than prediction uncertainty.
Stars
17
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 08, 2023
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/seedatnabeel/SSCP"
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...