This repository offers tools to analyze fairness in machine learning using the ABLNI metric applied to the Pima Diabetes dataset. It includes a complete SDK that provides visualizations and detailed reports. This enables users to evaluate bias in machine learning models effectively.
Before you begin, ensure your computer meets the following requirements:
- Operating System: Windows 10, macOS, or a recent Linux distribution
- Python Version: 3.7 or higher
- Free disk space: At least 500 MB
- Recommended RAM: Minimum 4 GB
To get the latest version of the fairness-analysis-pima software, visit this page to download: Download the Latest Release
On the releases page, look for the latest version. Click on the version number to see the files available for download. You will find an installer or executable file ready for your system.
After you download the file:
- Navigate to your Downloads folder.
- Double-click the downloaded file to start the installation.
- Follow the on-screen prompts to complete the installation.
If you are using a macOS device, you may need to drag the application to your Applications folder after unzipping.
- After installation, find the application on your system.
- Double-click the icon to launch fairness-analysis-pima.
- Follow the initial setup instructions to configure the software for your needs.
Once the application is running:
- Click on the Load Dataset button.
- Select the Pima Diabetes dataset from your files or use any other dataset you wish to analyze.
With your data loaded, you can start your analysis:
- Choose the metrics you want to analyze.
- Click on the Analyze button.
- Review the results displayed on your screen.
After completing your analysis:
- Click on the Generate Report button.
- Choose the format in which you want the report (PDF or HTML).
- Save the report to your desired location.
- Comprehensive analysis of ML fairness
- Visualizations to depict data relationships
- Detailed reporting capabilities
- Support for various datasets, including Pima Diabetes
If you run into issues, consider the following solutions:
- Ensure your Python version meets the requirement.
- Check if all necessary files were downloaded.
- Restart the application if it freezes or becomes unresponsive.
For additional help and community support:
- Visit the GitHub Issues page to report problems or ask questions.
- Join our discussions and share your experiences with other users.
Thank you to all contributors and the open-source community for supporting this project.
If you are interested in related subjects, you may explore:
- bias-detection
- data-science
- fairness in machine learning
- responsible AI practices
For more resources and updates, don't forget to check back frequently and stay engaged with the community.
Feel free to experiment with the tool and help us make it better by providing feedback!