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50 search results found

Interpret ⭐ 5,992

Fit interpretable models. Explain blackbox machine learning.

Generate Diverse Counterfactual Explanations for any machine learning model.

Dalex ⭐ 1,242

moDel Agnostic Language for Exploration and eXplanation

Awesome Interpretable Machine Learning ⭐ 856

Omnixai ⭐ 674

OmniXAI: A Library for eXplainable AI

Explainx ⭐ 375

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]

Modelstudio ⭐ 305

📍 Interactive Studio for Explanatory Model Analysis

Adversarial Explainable Ai ⭐ 235

💡 Adversarial attacks on explanations and how to defend them

Cnn Exposed ⭐ 157

🕵️‍♂️ Interpreting Convolutional Neural Network (CNN) Results.

Xaience ⭐ 100

All about explainable AI, algorithmic fairness and more

Fastshap ⭐ 96

Fast approximate Shapley values in R

Tsinterpret ⭐ 83

An Open-Source Library for the interpretability of time series classifiers

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Zennit Crp ⭐ 80

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

Explainable Machine Learning in Survival Analysis

Treeshap ⭐ 72

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Machine Learning ⭐ 68

Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.

Ml Fairness Framework ⭐ 68

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Machine Learning For High Risk Applications Book ⭐ 60

Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications

Information Bottlenecks for Attribution

Xaiaterum2020 ⭐ 49

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Prototree ⭐ 46

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

XAI based human-in-the-loop framework for automatic rule-learning.

Shapleyexplanationnetworks ⭐ 36

Implementation of the paper "Shapley Explanation Networks"

Model Agnostic Counterfactual Explanations

Kernelshap ⭐ 28

Efficient R implementation of SHAP

A Python package with explanation methods for extraction of feature interactions from predictive models

Plaquebox Paper ⭐ 18

Repo for Tang et al, bioRxiv 454793 (2018)

Xai Scholar ⭐ 16

Cross-field empirical trends analysis of XAI literature

Genetic programming method for explaining complex black-box models

PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)

MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.

Variable importance via oscillations

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

Awesome Time Series Explainability ⭐ 11

A list of (post-hoc) XAI for time series

Label Free Xai ⭐ 10

This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.

Article Information 2019 ⭐ 10

Article for Special Edition of Information: Machine Learning with Python

Quantified Sleep ⭐ 9

Quantified Sleep: Machine learning techniques for observational n-of-1 studies.

Explore/examine/explain/expose your model with the explabox!

Shapley_valuation ⭐ 6

PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]

Robust Counterfactuals ⭐ 6

Repo of the paper "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"

Deep learning for personalized interpretability for compositional health data

Robust regression algorithm that can be used for explaining black box models (R implementation)

Robust regression algorithm that can be used for explaining black box models (Python implementation)

Re-implementation of GP-GOMEA that attempts to be simpler to understand and use than the original.

XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.

Visualime ⭐ 5

Implementation of LIME focused on producing user-centric local explanations for image classifiers.

Xi Method ⭐ 5

Xai Papers ⭐ 5

1-50 of 50 search results

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