yramon / edc
Heuristic best-first algorithm for computing Evidence Counterfactuals (SEDC): explaining the model predictions of any classifier using a minimal set of features, such that removing these features results in a predicted class change.
☆15Updated 4 years ago
Alternatives and similar repositories for edc:
Users that are interested in edc are comparing it to the libraries listed below
- Model Agnostic Counterfactual Explanations☆87Updated 2 years ago
- Adversarial Attacks on Post Hoc Explanation Techniques (LIME/SHAP)☆82Updated 2 years ago
- Hybrid algorithm based on SEDC and LIME for computing Evidence Counterfactuals (LIME-Counterfactual): explaining the model predictions of…☆11Updated 4 years ago
- A lightweight implementation of removal-based explanations for ML models.☆59Updated 3 years ago
- XAI-Bench is a library for benchmarking feature attribution explainability techniques☆63Updated 2 years ago
- Contrastive Explanation (Foil Trees), developed at TNO/Utrecht University☆45Updated 2 years ago
- A Python package for unwrapping ReLU DNNs☆70Updated last year
- CEML - Counterfactuals for Explaining Machine Learning models - A Python toolbox☆43Updated 8 months ago
- All about explainable AI, algorithmic fairness and more☆107Updated last year
- LOcal Rule-based Exlanations☆53Updated last year
- Application of the LIME algorithm by Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin to the domain of time series classification☆95Updated last year
- Modular Python Toolbox for Fairness, Accountability and Transparency Forensics☆77Updated last year
- Multi-Objective Counterfactuals☆41Updated 2 years ago
- Clairvoyance: a Unified, End-to-End AutoML Pipeline for Medical Time Series☆131Updated 2 years ago
- This repository provides details of the experimental code in the paper: Instance-based Counterfactual Explanations for Time Series Classi…☆18Updated 3 years ago
- python tools to check recourse in linear classification☆75Updated 4 years ago
- Explaining Anomalies Detected by Autoencoders Using SHAP☆40Updated 3 years ago
- ☆90Updated 3 years ago
- OpenXAI : Towards a Transparent Evaluation of Model Explanations☆242Updated 7 months ago
- ☆125Updated 3 years ago
- A toolbox for differentially private data generation☆131Updated last year
- Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems☆74Updated 3 years ago
- For calculating Shapley values via linear regression.☆67Updated 3 years ago
- A Natural Language Interface to Explainable Boosting Machines☆65Updated 8 months ago
- CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms☆286Updated last year
- Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831☆36Updated 2 years ago
- Supervised Local Modeling for Interpretability☆28Updated 6 years ago
- Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" ht…☆127Updated 4 years ago
- ☆17Updated last year
- For calculating global feature importance using Shapley values.☆267Updated last week