rcwylie / shapgpt

Using Shap values and ChatGPT to explain reasoning behind Random Forrest models decision.

Jupyter Notebook: Explaining Credit Fraud Predictions with SHAP and ChatGPT

This Jupyter Notebook provides a step-by-step guide to using SHAP (SHapley Additive exPlanations) values to interpret predictions made by a Random Forest model for credit fraud categorisation. Additionally, it leverages ChatGPT to provide natural language explanations for the model's predictions.

Setup Instructions:

Install Required Packages:

Make sure you have the necessary Python packages installed.

Get API Key for OpenAI GPT-3:

Obtain an API key from OpenAI GPT-3 by following the instructions on the OpenAI website.

Configure OpenAI API Key:

Set your OpenAI API key as a variable.

Usage:

Run the Jupyter Notebook: Open the Credit_Fraud_SHAP_Explanation.ipynb notebook in Jupyter and execute each cell step by step.

Explore SHAP Values:

Understand how the SHAP values contribute to the predictions made by the Random Forest model. Visualizations and explanations are provided to interpret the impact of each feature on individual predictions.

ChatGPT Explanations:

Utilize ChatGPT to generate natural language explanations for the model's predictions. The notebook demonstrates how to integrate ChatGPT using the OpenAI GPT-3 API for more human-friendly and interpretable explanations.

Modify for Your Use Case:

Feel free to adapt the notebook for your specific use case or dataset. Update the model, features, or integrate additional steps as needed.

Dependencies: Jupyter SHAP scikit-learn OpenAI GPT-3 License: This project is licensed under the MIT License - see the LICENSE file for details. Feel free to modify and use the code as needed.

Note: Ensure you handle your API keys securely and responsibly. The OpenAI API key is sensitive information, and it should not be exposed publicly or shared without proper precautions.