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
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:caption: Introduction
installation
usage
technical_details
metrics
:caption: Examples
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sequential
other
benchmark
:caption: API Reference
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api/add_api
api/modules
GitHub repository https://github.com/SquareResearchCenter-AI/BEExAI