Minimal, builder‑first implementation of conformal prediction — designed to make reliability guardrails in AI systems accessible, transparent, and easy to extend.
Conformal prediction is a mathematically rigorous framework pioneered by Vladimir Vovk for quantifying uncertainty in machine learning. This repo distills the concept down to its simplest working form, so practitioners and recruiters alike can see:
- How uncertainty sets are constructed
- Why conformal prediction matters for Responsible AI
- How reliability can be embedded into everyday ML workflows
- Minimal code footprint — clear, readable Python implementation
- Step‑by‑step walkthroughs — from data split to prediction sets
- Extensible design — easy to adapt for classification, regression, or time series
- AI Safety & Reliability — guardrails for high‑stakes predictions
- Security Environments — uncertainty‑aware anomaly detection
- Forecasting & Time Series — confidence sets for future values
This project extends the lineage of Vladimir Vovk’s conformal prediction into modern Responsible AI practice, and the pragmatic instruction of my teacher Valeriy Monokhin, a leading promoter of conformal prediction in applied forecasting and time series (https://github.com/valeman/awesome-conformal-prediction). It demonstrates how reliability can be staged as a portfolio artifact — proof that engineering maturity and safety can coexist.