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  "Package": "basemodels",
  "Title": "Baseline Models for Classification and Regression",
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  "Authors@R": "c(\nperson(given = \"Ying-Ju\",\nfamily = \"Chen\",\nrole = c(\"aut\", \"cre\"),\nemail = \"ychen4@udayton.edu\",\ncomment = c(ORCID = \"0000-0002-6444-6859\")),\nperson(given = \"Fadel M.\",\nfamily = \"Megahed\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0003-2194-5110\")),\nperson(given = \"L. Allison\",\nfamily = \"Jones-Farmer\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0002-1529-1133\")),\nperson(given = \"Steven E.\",\nfamily = \"Rigdon\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0001-7668-0899\"))\n)",
  "Description": "Providing equivalent functions for the dummy classifier\nand regressor used in 'Python' 'scikit-learn' library. Our goal\nis to allow R users to easily identify baseline performance for\ntheir classification and regression problems. Our baseline\nmodels use no predictors, and are useful in cases of class\nimbalance, multiclass classification, and when users want to\nquickly identify how much improvement their statistical and\nmachine learning models are over several baseline models. We\nuse a \"better\" default (proportional guessing) for the dummy\nclassifier than the 'Python' implementation (\"prior\", which is\nthe most frequent class in the training set). The functions in\nthe package can be used on their own, or introduce methods\nnamed 'dummy_regressor' or 'dummy_classifier' that can be used\nwithin the caret package pipeline.",
  "License": "MIT + file LICENSE",
  "URL": "https://github.com/Ying-Ju/basemodels",
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  "Repository": "https://ying-ju.r-universe.dev",
  "Date/Publication": "2023-07-30 21:30:06 UTC",
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  "Packaged": {
    "Date": "2026-05-27 08:49:34 UTC",
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  "Author": "Ying-Ju Chen [aut, cre] (ORCID:\n<https://orcid.org/0000-0002-6444-6859>),\nFadel M. Megahed [aut] (ORCID: <https://orcid.org/0000-0003-2194-5110>),\nL. Allison Jones-Farmer [aut] (ORCID:\n<https://orcid.org/0000-0002-1529-1133>),\nSteven E. Rigdon [aut] (ORCID: <https://orcid.org/0000-0001-7668-0899>)",
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