Explainable AI (XAI) Examples Repository

Overview

This repository offers practical examples and educational resources to help you understand Explainable AI (XAI). It includes Jupyter notebooks and Python scripts that demonstrate the use of various XAI frameworks, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). The aim is to provide a hands-on approach to interpreting machine learning model predictions and to shed light on the decision-making processes of complex algorithms.

Contents

• Notebooks/ - Contains Jupyter notebooks demonstrating the usage of LIME and SHAP for different types of data (tabular, text, and images).
• Tools/ - Includes Python scripts and utilities that support the implementation of XAI techniques.

Getting Started

To explore the examples in this repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/Naviden/Introduction-to-XAI.git

2. Install the required dependencies:

   pip install -r requirements.txt

3. Navigate to the Notebooks directory and open the Jupyter notebooks:

   cd Notebooks/
   jupyter notebook

Contributing

Contributions are welcome! If you'd like to add new examples, enhance existing ones, or suggest additional XAI frameworks to include, please submit a pull request or open an issue.