An extension of XGBoost to probabilistic modelling

Python package for conformal prediction

An extension of LightGBM to probabilistic modelling

Conformalized Quantile Regression

Quantile Regression Forests compatible with scikit-learn.

An extension of CatBoost to probabilistic modelling

Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.

👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster

Valid and adaptive prediction intervals for probabilistic time series forecasting

Bringing back uncertainty to machine learning.

Official implementation of the paper "PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction" by Eli Simhayev, Gilad Katz and Lior Rokach.

Prediction and inference procedures for synthetic control methods with multiple treated units and staggered adoption.

Adaptive Conformal Prediction Intervals (ACPI) is a Python package that enhances the Predictive Intervals provided by the split conformal approach by employing a weighting strategy.

Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation

Prediction Intervals with specific value prediction

**curve_fit_utils** is a Python module containing useful tools for curve fitting

Deep joint mean and quantile regression for spatio-temporal problems

Implementation of Conformal Convolution T-learner (CCT) and Conformal Monte Carlo (CMC) learner

DualAQD: Dual Accuracy-quality-driven Prediction Intervals

Prediction intervals for trees using conformal intervals. Docs at https://pitci.readthedocs.io/en/latest/