Cross-field empirical trends analysis of XAI literature

Code for paper "Towards Training GNNs using Explanation Directed Message Passing"

XAI-Analytics is a tool that opens the black-box of machine learning. It helps the user to understand the decision-making process of machine learning models.

Replication package for the KNOSYS paper titled "An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability".

This repository aims to explain state-of-the-art Fake News Detection models. Huggingface module is intended for explaining only-text-based fake news detection models in Transformers. GNNFakeNews module is an attempt to explain GNNs that are hybrid models for fake news detection using GNNExplainer

The notebook on the main topic of interpretable machine learning is a descriptive and instructive analysis of a car data set from a public source.

Anomaly Detection Web Application using Dynamic Ensembles and SHAP Explanations

Projects for Praxis Data Science

https://github.com/jphall663 -- https://github.com/jphall663/awesome-machine-learning-interpretability -- https://www.youtube.com/watch?v=d6j6bofhj2M&list=PLV8yxwGOxvvovp-j6ztxhF3QcKXT6vORU&index=3

This repository focuses on classifying cancer types using ML algorithms. It includes data preprocessing, feature selection, model training, and evaluation using Logistic Regression, Decision Trees, Random Forest, SVC, KNN, and Naive Bayes. Explainable AI techniques like SHAP and LIME are implemented for transparency.