# valeman/awesome-conformal-prediction

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

Repository: https://github.com/valeman/awesome-conformal-prediction
Canonical: https://ross.abutalabs.com/products/awesome-conformal-prediction
License: NOASSERTION
License Family: other
Topics: conformal-prediction, python, r, machinelearning, datascience, deeplearning, awesome, awesome-list, probability, uncertainty-quantification, uncertainty, uncertainty-estimation, probability-distribution, probability-distributions, machine-learning, ai, explainable-ai, statistics, robust-ml
Last push: 2026-08-25T10:44:29+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 8, longevity 100
- inputs: {"age_days": 1727, "days_push": 8, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1291, forks 118 (observed 2026-08-28T04:04:15.734757+00:00)

## What it is
A curated awesome-list of resources on conformal prediction, including tutorials, videos, books, papers, theses, and open-source libraries in Python and R. It serves as the canonical reference hub for distribution-free uncertainty quantification methods in machine learning.

## Use cases
- learn conformal prediction from scratch
- find python libraries for prediction intervals
- quantify uncertainty in machine learning models
- find papers on conformal inference
- build calibrated prediction sets for classification
- estimate uncertainty for LLM outputs
- find tutorials on distribution-free inference

## When to choose
- you need a comprehensive, regularly updated index of conformal prediction resources
- you are researching or learning uncertainty quantification
- you want links to production-ready conformal prediction libraries

## When to avoid
- you need a working software library rather than a resource list
- you need guaranteed licensing clarity (license is unspecified)

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, developer-tools, documentation
- domain: machine-learning, data-science, artificial-intelligence, tutorials, awesome-lists
- platform: python, cross-platform
- tags: conformal-prediction, uncertainty-quantification, prediction-intervals, awesome-list, model-calibration, trustworthy-ml, statistics

## Member repositories
- valeman/awesome-conformal-prediction (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:15.734757+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:54:49.799052+00:00, confidence not recorded.
  - readme: https://github.com/valeman/awesome-conformal-prediction (fetched 2026-08-28T04:04:15.734757+00:00, sha 2b56e937b28c)
- Data as of 2026-08-30T08:39:29.467469+00:00.