IBM / UQ360
Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.
☆259Updated 6 months ago
Alternatives and similar repositories for UQ360:
Users that are interested in UQ360 are comparing it to the libraries listed below
- CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms☆286Updated last year
- Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true cla…☆235Updated 2 years ago
- Conformalized Quantile Regression☆263Updated 2 years ago
- Model Agnostic Counterfactual Explanations☆86Updated 2 years ago
- ☆468Updated 6 months ago
- Datasets derived from US census data☆251Updated 9 months ago
- Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations☆584Updated 2 weeks ago
- All about explainable AI, algorithmic fairness and more☆107Updated last year
- Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…☆144Updated 2 years ago
- A Library for Uncertainty Quantification.☆897Updated last week
- A library for uncertainty quantification based on PyTorch☆121Updated 3 years ago
- Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertaint…☆617Updated 2 years ago
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆239Updated 6 months ago
- Editing machine learning models to reflect human knowledge and values☆124Updated last year
- Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, ca…☆166Updated 9 months ago
- An automated machine learning tool aimed to facilitate AutoML research.☆96Updated 5 months ago
- A Python sandbox for decision making in dynamics☆422Updated last year
- Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"☆271Updated 2 years ago
- Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.☆130Updated 4 years ago
- A Python package for building Bayesian models with TensorFlow or PyTorch☆173Updated 2 years ago
- Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human…☆73Updated 2 years ago
- Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"☆456Updated last year
- ☆236Updated 2 years ago
- For calculating global feature importance using Shapley values.☆264Updated last week
- ☆149Updated 2 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 3 years ago
- WeightedSHAP: analyzing and improving Shapley based feature attributions (NeurIPS 2022)☆160Updated 2 years ago
- Interpretability and explainability of data and machine learning models☆1,655Updated 7 months ago
- Weakly Supervised End-to-End Learning (NeurIPS 2021)☆157Updated last year
- scikit-activeml: Python library for active learning on top of scikit-learn☆162Updated this week