/ExplainableAI

🌐 Uncover the magic behind complex models by breaking down their predictions into understandable chunks using these XAI techniques.

Primary LanguageJupyter Notebook

Explainable AI Notebook: LIME, SHAP, Counterfactual Explanations 🧠📊

Overview ℹ️

This notebook showcases the power of LIME, SHAP, and Counterfactual Explanations in unraveling the mysteries of machine learning models! 🌟

Purpose 🎯

Uncover the magic behind complex models by breaking down their predictions into understandable chunks using these XAI techniques. 🌐

Contents 📝

  • LIME (Local Interpretable Model-agnostic Explanations): Get up close and personal with individual predictions! 🎯
  • SHAP (SHapley Additive exPlanations): Discover the global importance of features! 🌍
  • Counterfactual Explanations: Peek into alternate realities to understand decision boundaries! 🔍

Requirements 🛠️

  • Python 3.x
  • Jupyter Notebook
  • Required libraries (Check requirements.txt)

Usage 🚀

  1. Installation: Ensure you have the necessary libraries using pip install -r requirements.txt.
  2. Notebook Execution: Fire up Jupyter Notebook or JupyterLab and open ExplainableAI_LIME_SHAP_Counterfactuals.ipynb.
  3. Run the Cells: Execute each cell in order to witness the magic of these XAI methods!

Note 📌

  • This notebook comes packed with sample datasets and models for easy experimentation.
  • Customization: Feel free to apply these methods to your own data and models by tweaking the code.

Citation 📄

If you find this notebook helpful, consider citing the relevant papers or libraries for LIME, SHAP, and Counterfactual Explanations. 🙏

Resources 📚

Contributors 👥