A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.

counterfactuals: An R package for Counterfactual Explanation Methods

SLISEMAP: Combining supervised dimensionality reduction with local explanations

Local Universal Rule-based Explanations

Generating global explanations from local ones

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

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

Interpretable time-series forecasting on the AirPassengers dataset using ARIMA, XGBoost, LIME, and SHAP

Code for paper "XPROAX - Local explanations for text classification with progressive neighborhood approximation", DSAA 2021 (https://ieeexplore.ieee.org/abstract/document/9564153). Repository maintained by Yi Cai.

Newton–Puiseux for CVNNs: complete toolkit for uncertainty mining, confidence calibration and local symbolic-numeric analysis on ECG (MIT-BIH) and wireless IQ data (RadioML 2016.10A).

Interpreting Categorical Data Classifiers using Explanation-based Locality

This repository presents a comprehensive research paper exploring the role of Explainable Artificial Intelligence (XAI) in modern Machine Learning. It aims to shed light on the interpretability of 'black-box' models like Neural Networks, Explainable AI and highlights the need for transparent, human-understandable ML systems.