This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance.
The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization to render contributions as bar plots, highlighted text, or segmented image regions.
Capabilities cover several data domains, including text classification analysis, tabular feature influence, and regression model diagnostics. For image data, the tool utilizes pixel contribution analysis and segment visualization to highlight areas driving a classification decision.
The library includes utilities for data preprocessing, such as continuous feature binning, categorical feature management, and mixed feature processing.