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iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios

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Explainable Artificial Intelligence (xAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1901))

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Abstract

Post-hoc explanation techniques such as the well-established partial dependence plot (PDP), which investigates feature dependencies, are used in explainable artificial intelligence (XAI) to understand black-box machine learning models. While many real-world applications require dynamic models that constantly adapt over time and react to changes in the underlying distribution, XAI, so far, has primarily considered static learning environments, where models are trained in a batch mode and remain unchanged. We thus propose a novel model-agnostic XAI framework called incremental PDP (iPDP) that extends on the PDP to extract time-dependent feature effects in non-stationary learning environments. We formally analyze iPDP and show that it approximates a time-dependent variant of the PDP that properly reacts to real and virtual concept drift. The time-sensitivity of iPDP is controlled by a single smoothing parameter, which directly corresponds to the variance and the approximation error of iPDP in a static learning environment. We illustrate the efficacy of iPDP by showcasing an example application for drift detection and conducting multiple experiments on real-world and synthetic data sets and streams.

M. Muschalik and F. Fumagalli—denotes equal contribution.

We gratefully acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): TRR 318/1 2021 - 438445824.

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Notes

  1. 1.

    iPDP is part of the iXAI framework at https://github.com/mmschlk/iXAI.

  2. 2.

    The debiasing factor ensures that for a constant sequence the exponential moving average remains constant and will be theoretically justified in Sect. 3.3.

  3. 3.

    All experiments are based on sklearn, pytorch and the river online learning framework. All datasets are publicly available and described in the supplement C.1. The code to reproduce the experiments can be found at https://github.com/mmschlk/iPDP-On-Partial-Dependence-Plots-in-Dynamic-Modeling-Scenarios.

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Authors and Affiliations

  1. LMU Munich, Geschwister-Scholl-Platz 1, 80539, Munich, Germany

    Maximilian Muschalik, Rohit Jagtani & Eyke Hüllermeier

  2. Munich Center for Machine Learning (MCML), Munich, Germany

    Maximilian Muschalik & Eyke Hüllermeier

  3. CITEC, Bielefeld University, Inspiration 1, 33619, Bielefeld, Germany

    Fabian Fumagalli & Barbara Hammer

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  1. Maximilian Muschalik
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Correspondence to Maximilian Muschalik .

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  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

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Muschalik, M., Fumagalli, F., Jagtani, R., Hammer, B., Hüllermeier, E. (2023). iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. In: Longo, L. (eds) Explainable Artificial Intelligence. xAI 2023. Communications in Computer and Information Science, vol 1901. Springer, Cham. https://doi.org/10.1007/978-3-031-44064-9_11

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  • DOI: https://doi.org/10.1007/978-3-031-44064-9_11

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