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Article Dans Une Revue Neurocomputing Année : 2023

teex: A toolbox for the evaluation of explanations

Résumé

We present teex, a Python toolbox for the evaluation of explanations. teex focuses on the evaluation of local explanations of the predictions of machine learning models by comparing them to ground-truth explanations. It supports several types of explanations: feature importance vectors, saliency maps, decision rules, and word importance maps. A collection of evaluation metrics is provided for each type. Real-world datasets and generators of synthetic data with ground-truth explanations are also contained within the library. teex contributes to research on explainable AI by providing tested, streamlined, user-friendly tools to compute quality metrics for the evaluation of explanation methods. Source code and a basic overview can be found at github.com/chus-chus/teex, and tutorials and full API documentation are at teex.readthedocs.io.

Dates et versions

hal-04468368 , version 1 (20-02-2024)

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Citer

Jesus Antonanzas, Yunzhe Jia, Eibe Frank, Albert Bifet, Bernhard Pfahringer. teex: A toolbox for the evaluation of explanations. Neurocomputing, 2023, 555, pp.126642. ⟨10.1016/J.NEUCOM.2023.126642⟩. ⟨hal-04468368⟩
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