This notebook showcases the power of LIME, SHAP, and Counterfactual Explanations in unraveling the mysteries of machine learning models! 🌟
Uncover the magic behind complex models by breaking down their predictions into understandable chunks using these XAI techniques. 🌐
- 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! 🔍
- Python 3.x
- Jupyter Notebook
- Required libraries (Check
requirements.txt
)
- Installation: Ensure you have the necessary libraries using
pip install -r requirements.txt
. - Notebook Execution: Fire up Jupyter Notebook or JupyterLab and open
ExplainableAI_LIME_SHAP_Counterfactuals.ipynb
. - Run the Cells: Execute each cell in order to witness the magic of these XAI methods!
- 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.
If you find this notebook helpful, consider citing the relevant papers or libraries for LIME, SHAP, and Counterfactual Explanations. 🙏
- Semah Kadri - Contact details