{ "_id": "6ac3e0cd34f9a32edea23118", "Package": "midr", "Type": "Package", "Title": "Learning from Black-Box Models by Maximum Interpretation\nDecomposition", "Version": "0.6.2.900", "Authors@R": "c(\nperson(\"Ryoichi\", \"Asashiba\", email = \"ryoichi.asashiba@gmail.com\",\nrole = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0001-9532-7000\")),\nperson(\"Hirokazu\", \"Iwasawa\", role = c(\"aut\")),\nperson(\"Reiji\", \"Kozuma\", role = c(\"ctb\"))\n)", "Description": "The goal of 'midr' is to provide a model-agnostic method\nfor interpreting and explaining black-box predictive models by\ncreating a globally interpretable surrogate model. The package\nimplements 'Maximum Interpretation Decomposition' (MID), a\nfunctional decomposition technique that finds an optimal\nadditive approximation of the original model. This\napproximation is achieved by minimizing the squared error\nbetween the predictions of the black-box model and the\nsurrogate model. The theoretical foundations of MID are\ndescribed in Iwasawa & Matsumori (2025) [Forthcoming], and the\npackage itself is detailed in Asashiba et al. 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