An extension of XGBoost to probabilistic modelling
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Updated
Jan 31, 2024 - Python
An extension of XGBoost to probabilistic modelling
Conformal classifiers, regressors and predictive systems
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.
Valid and adaptive prediction intervals for probabilistic time series forecasting
👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster
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.
Prediction Intervals with specific value prediction
Deep joint mean and quantile regression for spatio-temporal problems
**curve_fit_utils** is a Python module containing useful tools for curve fitting
Implementation of Conformal Convolution T-learner (CCT) and Conformal Monte Carlo (CMC) learner
Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation
Prediction intervals for trees using conformal intervals. Docs at https://pitci.readthedocs.io/en/latest/
DualAQD: Dual Accuracy-quality-driven Prediction Intervals
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