LIME (Local Interpretable Model-Agnostic Explanations)
LIME is an open-source Python library that explains the predictions of any machine learning classifier by approximating it locally with an interpretable model. It is model-agnostic and particularly useful for understanding and debugging black-box models.
Key Features
Local Interpretability: Generates simple, human-understandable explanations for individual predictions.
Model-Agnostic: Works with any classifier, including ensemble models, neural networks, and SVMs.
Visualization Support: Provides plots to illustrate feature contributions for predictions.
Broad Compatibility: Easily integrates with scikit-learn, TensorFlow, XGBoost, and more.
Supports Tabular, Text, and Image Data: Versatile tool for multiple AI domains.
Example Use Cases
Explaining predictions in credit scoring or medical diagnosis models
Debugging and validating machine learning model behavior
Building trust and transparency into black-box models
Supporting regulatory compliance in high-stakes AI applications