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An Analysis of LIME for Text Data

Dina Mardaoui 1 Damien Garreau 2, 3
3 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems, UNS - Université Nice Sophia Antipolis (... - 2019), JAD - Laboratoire Jean Alexandre Dieudonné
Abstract : Text data are increasingly handled in an automated fashion by machine learning algorithms. But the models handling these data are not always well-understood due to their complexity and are more and more often referred to as "black-boxes." Interpretability methods aim to explain how these models operate. Among them, LIME has become one of the most popular in recent years. However, it comes without theoretical guarantees: even for simple models, we are not sure that LIME behaves accurately. In this paper, we provide a first theoretical analysis of LIME for text data. As a consequence of our theoretical findings, we show that LIME indeed provides meaningful explanations for simple models, namely decision trees and linear models.
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Contributor : Damien Garreau <>
Submitted on : Monday, October 26, 2020 - 8:54:28 AM
Last modification on : Tuesday, October 27, 2020 - 3:32:21 AM

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  • HAL Id : hal-02977786, version 1
  • ARXIV : 2010.12487



Dina Mardaoui, Damien Garreau. An Analysis of LIME for Text Data. 2020. ⟨hal-02977786⟩



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