midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

Ryoichi Asashiba; Reiji Kozuma; Hirokazu Iwasawa

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arXiv · 2506.08338

midr: Learning from Black-Box Models by Maximum Interpretation Decomposition

Ryoichi Asashiba·Reiji Kozuma·Hirokazu Iwasawa

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Abstract

The use of appropriate methods of Interpretable Machine Learning (IML) and eXplainable Artificial Intelligence (XAI) is essential for adopting black-box predictive models in fields where model and prediction explainability is required. As a novel tool for interpreting black-box models, we introduce the R package midr, which implements Maximum Interpretation Decomposition (MID). MID is a functional decomposition approach that derives a low-order additive representation of a black-box model by minimizing the squared error between the model's prediction function and this additive representation. midr enables learning from black-box models by constructing a global surrogate model with advanced analytical capabilities. After reviewing related work and the theoretical foundation of MID, we demonstrate the package's usage and discuss some of its key features.

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Ryoichi Asashiba, Reiji Kozuma, Hirokazu Iwasawa. 2025-06-10. midr: Learning from Black-Box Models by Maximum Interpretation Decomposition. https://arxiv.org/abs/2506.08338

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