Name: Explanation Groves - Gero Szepannek, Stralsund University of Applied Science
Start: 2024-07-10T11:30:00+0200
End: 2024-07-10T11:50:00+0200

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8 - 11 July, 2024
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Explanation Groves - Gero Szepannek, Stralsund University of Applied Science

The increasing popularity of machine learning in many application fields has increased the demand in methods of explainable machine learning as eg provided by the packages DALEX (Biecek, 2018) and iml (Molnar, 2018). In turn, comparatively few research has been dedicated to the limits of explaining complex machine learning models (Rudin, 2019, Szepannek and Lübke, 2022). Explanation groves (Szepannek and v. Holt, 2024) are presented as a tool to extract a set of understandable rules for explanation of arbitrary machine learning models. The degree of complexity of the resulting explanation can defined be the user. This allows to analyze the trade off between the complexity of a given explanation and how well it represents the original model. The corresponding R package xgrove (Szepannek, 2023) is demonstrated. Biecek P (2018). https://jmlr.org/papers/v19/18-416.html Molnar C, Bischl B, Casalicchio G (2018). doi:10.21105/joss.00786 Rudin, C (2019). doi:10.1038/s42256-019-0048-x Szepannek G (2023). https://CRAN.R-project.org/package=xgrove Szepannek, G, v. Holt, B (2024). doi:10.1007/s41237-023-00205-2 Szepannek, G, Lübke, K (2022). doi:10.1007/s13218-022-00764-8

Wednesday July 10, 2024 11:30 - 11:50 CEST
Wolfgangsee

Machine learning and AI

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