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  "Title": "Model Agnostic Instance Level Variable Attributions",
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  "Authors@R": "c(person(\"Przemyslaw\", \"Biecek\", email = \"przemyslaw.biecek@gmail.com\", role = c(\"aut\", \"cre\"), \ncomment = c(ORCID = \"0000-0001-8423-1823\")),\nperson(\"Alicja\", \"Gosiewska\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6563-5742\")),\nperson(\"Hubert\", \"Baniecki\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6661-5364\")),\nperson(\"Adam\", \"Izdebski\", role = \"aut\"),\nperson(\"Dariusz\", \"Komosinski\", role = \"ctb\"))",
  "Description": "Model agnostic tool for decomposition of predictions from\nblack boxes. Supports additive attributions and attributions\nwith interactions. The Break Down Table shows contributions of\nevery variable to a final prediction. The Break Down Plot\npresents variable contributions in a concise graphical way.\nThis package works for classification and regression models. It\nis an extension of the 'breakDown' package (Staniak and Biecek\n2018) <doi:10.32614/RJ-2018-072>, with new and faster\nstrategies for orderings. It supports interactions in\nexplanations and has interactive visuals (implemented with\n'D3.js' library). The methodology behind is described in the\n'iBreakDown' article (Gosiewska and Biecek 2019)\n<arXiv:1903.11420> This package is a part of the 'DrWhy.AI'\nuniverse (Biecek 2018) <arXiv:1806.08915>.",
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