Implementation of Model-Agnostic Graph Explainability Technique from Scratch in PyTorch

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.

A new benchmark for graph neural network explainer methods

How to use SHAP to interpret machine learning models

Use of Machine Learning and Deep Learning Algorithms to recommend best clinical options to health professionals in South Africa

Final year project, exploring the field of quantum machine learning.

XMLX GitHub configuration

BBBP Explainer is a code to generate structural alerts of blood-brain barrier penetrating and non-penetrating drugs using Local Interpretable Model-Agnostic Explanations (LIME) of machine learning models from BBBP dataset.

This module extends the kernel SHAP method (as introduced by Lundberg and Lee (2017)) which is local in nature, to a method that computes global SHAP values.

Graduate research project in computer vision and deep learning explainability

Getting explanations for predictions made by black box models.

A Novel Optimization Objective for Explainable and Customizable Learning of Multi-Classifiers

[Frontiers in AI Journal] Implementation of the paper "Interpreting Vision and Language Generative Models with Semantic Visual Priors"

Predicting whether an African country will be in recession or not with advanced machine learning techniques involving class imbalance, cost-sensitive learning and explainable machine learning

Measuring galaxy environmental distance scales with GNNs and explainable ML models

Explanation-guided boosting of machine learning evasion attacks.

Ths repo has the list of Interesting Literature in the domain of XAI

This repository consists the supplemental materials of the paper "Decomposition of Expected Goal Models: Aggregated SHAP Values for Analyzing Scoring Potential of Player/Team".

This repository contains a comprehensive implementation of gradient descent for linear regression, including visualizations and comparisons with ordinary least squares (OLS) regression. It also includes an additional implementation for multiple linear regression using gradient descent.

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.