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Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models

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  • Giorgio Visani
  • Enrico Bagli
  • Federico Chesani
  • Alessandro Poluzzi
  • Davide Capuzzo

Abstract

Nowadays we are witnessing a transformation of the business processes towards a more computation driven approach. The ever increasing usage of Machine Learning techniques is the clearest example of such trend. This sort of revolution is often providing advantages, such as an increase in prediction accuracy and a reduced time to obtain the results. However, these methods present a major drawback: it is very difficult to understand on what grounds the algorithm took the decision. To address this issue we consider the LIME method. We give a general background on LIME then, we focus on the stability issue: employing the method repeated times, under the same conditions, may yield to different explanations. Two complementary indices are proposed, to measure LIME stability. It is important for the practitioner to be aware of the issue, as well as to have a tool for spotting it. Stability guarantees LIME explanations to be reliable therefore a stability assessment, made through the proposed indices, is crucial. As a case study, we apply both Machine Learning and classical statistical techniques to Credit Risk data. We test LIME on the Machine Learning algorithm and check its stability. Eventually, we examine the goodness of the explanations returned.

Suggested Citation

  • Giorgio Visani & Enrico Bagli & Federico Chesani & Alessandro Poluzzi & Davide Capuzzo, 2022. "Statistical stability indices for LIME: Obtaining reliable explanations for machine learning models," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 73(1), pages 91-101, January.
  • Handle: RePEc:taf:tjorxx:v:73:y:2022:i:1:p:91-101
    DOI: 10.1080/01605682.2020.1865846
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    Cited by:

    1. Haipeng Liu & Jiangtao Wang & Yayuan Geng & Kunwei Li & Han Wu & Jian Chen & Xiangfei Chai & Shaolin Li & Dingchang Zheng, 2022. "Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning," IJERPH, MDPI, vol. 19(17), pages 1-14, August.
    2. Alejandra de la Rica Escudero & Eduardo C. Garrido-Merchan & Maria Coronado-Vaca, 2024. "Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent," Papers 2407.14486, arXiv.org.
    3. Ricardo Muller & Marco Schreyer & Timur Sattarov & Damian Borth, 2022. "RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations," Papers 2209.09157, arXiv.org.
    4. Janssens, Bram & Schetgen, Lisa & Bogaert, Matthias & Meire, Matthijs & Van den Poel, Dirk, 2024. "360 Degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, Elsevier, vol. 317(2), pages 366-381.

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