Alibi Explain: Algorithms for Explaining Machine Learning Models
Published in Journal of machine learning resear... (2021-01-01)
TL
TL;DR
This work introduces Alibi Explain, an open-source Python library for explaining predictions of machine learning models, which features state-of-the-art explainability algorithms for classification and regression models.
Abstract
We introduce Alibi Explain , an open-source Python library for explaining predictions of machine learning models ( https://github.com/SeldonIO/alibi ). The library features state-of-the-art explainability algorithms for classification and regression models. The algorithms cover both the model-agnostic (black-box) and model-specific (white-box) setting, cater for multiple data types (tabular, text, images) and explanation scope (local and global explanations). The library exposes a unified API enabling users to work with explanations in a consistent way. Alibi adheres to best development practices featuring extensive testing of code correctness and algorithm convergence in a continuous integration environment. The library comes with extensive documentation of both usage and theoretical background of methods, and a suite of worked end-to-end use cases. Alibi aims to be a production-ready toolkit with integrations into machine learning deployment platforms such as Seldon Core and KFServing , and distributed explanation capabilities using Ray .
References24 items
1
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Computer ScienceMathematics
2
This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Tensorflow: a System for Large-scale Machine Learning Tensorflow: a System for Large-scale Machine Learning
Computer Science
3
Captum: A unified and generic model interpretability library for PyTorch
4