Visualization of many Clustering Algorithms, via Notebook or GUI

This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

This notebook can be downloaded, tested and modified with Google Colab and aims at explainable how a Decision Tree is built. It is also coupled with a Medium article.

Explainable AI (XAI) Notebooks

Collection and implementation of a variety of machine learning code examples (notebooks and Python scripts) and projects.

Implementing text classification algorithms using the 20 newsgroups datasets, with python

Repository containing sample datasets, models and notebooks to start using EXPAI.

Methods to improve the explainability of machine learning models, while still being performant models. This repository presents an implementation of Google Brain's Team Distilling a neural network into a Soft Decision Tree by Nicholas Frosst and Geoffrey Hinton.

This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist exp…

A collection of notebooks to explore bias, fairness and explainability of machine learning models

This notebook demonstrates a number of saliency mask techniques, augmented with the SmoothGrad technique, using the VGG16, AlexNet, InceptioNet, MobileNet convolutional neural network in TF2.