Observation-specific explanations through scattered data approximation

Valentina Ghidini; Michael Multerer; Jacopo Quizi; Rohan Sen

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arXiv · 2404.08747

Observation-specific explanations through scattered data approximation

Valentina Ghidini·Michael Multerer·Jacopo Quizi·Rohan Sen

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Abstract

This work introduces the definition of observation-specific explanations to assign a score to each data point proportional to its importance in the definition of the prediction process. Such explanations involve the identification of the most influential observations for the black-box model of interest. The proposed method involves estimating these explanations by constructing a surrogate model through scattered data approximation utilizing the orthogonal matching pursuit algorithm. The proposed approach is validated on both simulated and real-world datasets.

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stat.ML cs.AI cs.LG cs.NA math.NA

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Valentina Ghidini, Michael Multerer, Jacopo Quizi, Rohan Sen. 2024-04-12. Observation-specific explanations through scattered data approximation. https://arxiv.org/abs/2404.08747

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