scikit-learn-contrib/forest-confidence-interval

A scikit-learn-compatible library for estimating prediction intervals and controlling risks, based on conformal predictions.

👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.

relplot: Utilities for measuring calibration and plotting reliability diagrams

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

R package to compute and plot predictions, slopes, marginal means, and comparisons (contrasts, risk ratios, odds, etc.) for over 100 classes of statistical and ML models. Conduct linear and non-linear hypothesis tests, or equivalence tests. Calculate uncertainty estimates using the delta method, bootstrapping, or simulation-based inference

scikit-learn contrib estimators

Natural Gradient Boosting for Probabilistic Prediction

Python implementation of binary and multi-class Venn-ABERS calibration