This project demonstrates the use of LIME (Local Interpretable Model-Agnostic Explanations) to explain predictions made by a logistic regression model trained on the Iris dataset. The repository is structured following industry best practices, including thorough documentation, testing, and continuous integration.
- Data Loading: Load the Iris dataset using scikit-learn.
- Model Training: Train a logistic regression classifier.
- LIME Integration: Use LIME to generate explanations for model predictions.
- Testing: Unit tests for model training and explanation generation.
- CI/CD: GitHub Actions for continuous integration.
- Documentation: Detailed installation and usage guides.
Please refer to docs/installation.md for installation instructions.
An example usage script is provided in examples/run_explainer.py. For further details, see docs/usage.md.