Uncertainty Estimation Python Packages

ngboost

Natural Gradient Boosting for Probabilistic Prediction

quantile-forest

Quantile Regression Forests compatible with scikit-learn.

uncertainty-toolbox

Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

lightgbmlss

An extension of LightGBM to probabilistic modelling

puncc

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

xgboostlss

An extension of XGBoost to probabilistic modelling

pygpc

A Sensitivity and uncertainty analysis toolbox for Python based on the generalized polynomial chaos method

calibrated-explanations

Calibrated Explanations is an explanation method for uncertainty-aware explanations of classification and regression models.

uqlm

[JMLR 2026] UQLM: A Python package for LLM hallucination detection using uncertainty quantification

fiducio

Post-hoc calibration for 2D/3D semantic segmentation in PyTorch: temperature, vector, matrix and Dirichlet scaling, translation-invariant variants, and decision-preserving calibrators. Official code of "Rethinking Post-Hoc Calibration in Semantic Segmentation" (TMLR)

bayesian-torch

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

adfwi-torch

An Automatic Differentiation-based Waveform Inversion Framework Implemented in PyTorch.

seapig

Uncertainty based selection of compatible inputs

calibrain

Python toolbox for uncertainty estimation and calibration in EEG/MEG inverse source imaging.

nlp-uncertainty-zoo

Model zoo for different kinds of uncertainty quantification methods used in Natural Language Processing, implemented in PyTorch.

kid-ppg

KID-PPG package for heart rate extraction from photoplethysmography signals

masksembles

Official repository for the paper "Masksembles for Uncertainty Estimation" (CVPR 2021).

ngboost-release

Natural Gradient Boosting for Probabilistic Prediction

torchbayesian

PyTorch-native Bayesian neural networks for uncertainty quantification in deep learning

sentinelml

PyPLTool : Runtime trust layer for machine learning systems. Detects drift, uncertainty, and reliability risks in production ML models.

robust-deep-learning

A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.

photonai-conformal

Conformal Predictions using photonai

muben

MUBen: Benchmarking the Uncertainty of Molecular Representation Models