interpretable-machine-learning topic

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

ModelOriented

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

adaamko

Interpretability methods applied on image classifiers trained on MNIST and CIFAR10

arm-on

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

M-Nauta

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.

Crisp-Unimib

Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"

zju-vipa

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 Expla...

JonathanCrabbe

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]

ajsanjoaquin

The repository for the submission "Visualizing the Impact of Feature Attribution Baselines"

distillpub

Shapley Values with H2O AutoML Example (ML Interpretability)

SeanPLeary