Welcome to BEExAI documentation!
BEExAI is a Python library for benchmarking explainability methods on tabular data. It supports a wide range of explainability methods and evaluation metrics. It is designed to be easy to use and to allow fast obtention of benchmark results.
Major features include:
Automatic preprocessing of tabular data
Training of several models including scikit-learn and PyTorch Neural Network models.
Computation of attributions for explainability methods from Captum
Computation of evaluation metrics for explainability methods for robustness, faithfulness and complexity
Contents
GitHub repository https://github.com/SquareResearchCenter-AI/BEExAI