Methods for conformal/probabilistic prediction (Conformal Predictors, Venn-Predictors) are wrappers around Machine Learning algorithms, that provide guarantees about their predictions; generally, their sole assumption is exchangeability on data (weaker than standard i.i.d.). They were first proposed in the book “Algorithmic Learning in a Random World” (Vovk, Gammerman, Shafer; 2005).

**Conformal Predictors** (CP) allow limiting the errors committed by
a learning algorithm (“underlying algorithm”), in a multi-label classification
setting, to a desired significance level $\varepsilon$:
their accuracy is guaranteed to be at least $1-\varepsilon$.

**Venn-Predictors** (VP) output a set of probability distributions on the labels,
as a prediction for a new object $x$; one of these distributions is guaranteed to be
*perfectly calibrated*.

- Main Python implementation of Conformal Prediction: https://github.com/donlnz/nonconformist
- My Rust implementation of Conformal Prediction-related methods: https://github.com/gchers/random-world
- (Unsupported) My old Python implementation of Conformal Prediction: https://github.com/gchers/cpy