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.

Explainable AI (XAI) Notebooks

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.

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 Jupyter Notebooks exploring key topics in Artificial Intelligence, including recommender systems, explainable AI, reinforcement learning, and transformers.

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

Collection of notebooks accompanying a research paper on evaluating GHG emissions from hydroelectric, multipurpose and irrigation reservoirs in Myanmar

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

Modeling Agri-finance Credit Risk with Data & Machine Learning using Python (Jupyter Notebook)

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.

This repo contains code for predicting life satisfaction using machine learning and explainable AI, as published in Heliyon. It includes a Jupyter Notebook with data processing, model building, and result visualization using Python libraries. The analysis uses the SHILD dataset to explore factors influencing life satisfaction.

A Bachelor's Thesis project analyzing and comparing classifiers for breast cancer detection using fine needle aspiration biopsies. Includes Jupyter Notebooks for model training and evaluation, and a LaTeX document detailing the methodology and results. Features SHAP for explainable AI analysis.