An extension of XGBoost to probabilistic modelling

Python package for conformal prediction

An extension of LightGBM to probabilistic modelling

Quantile Regression Forests compatible with scikit-learn.

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

Bringing back uncertainty to machine learning.

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

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

An HR predictive analytics tool for forecasting the likely range of a worker’s future job performance using multiple ANNs with custom loss functions.

This module contains functions, bootStrapParamCI and bootStrapPredictInterval, that follow a bootstrap approach to produce confidence intervals for model parameters and prediction intervals for individual point predictions, respectively.

Analysis of Predictive inference with jackknife+, a new method for creating prediction intervals with stronger coverage guarantees