{ "_id": "6a1d71051d7bb097a0a50692", "Package": "IRon", "Type": "Package", "Title": "Solving Imbalanced Regression Tasks", "Version": "0.1.4", "Authors@R": "c(person(\"Nuno\", \"Moniz\", email = \"nmoniz2@nd.edu\",\nrole = c(\"cre\",\"aut\")),\nperson(\"Rita\", \"P. Ribeiro\", email = \"rpribeiro@dcc.fc.up.pt\",\nrole = \"aut\"),\nperson(\"Miguel\", \"Margarido\", email = \"miguelmargarido@gmail.com\",\nrole = \"ctb\"))", "Description": "Imbalanced domain learning has almost exclusively focused\non solving classification tasks, where the objective is to\npredict cases labelled with a rare class accurately. Such a\nwell-defined approach for regression tasks lacked due to two\nmain factors. First, standard regression tasks assume that each\nvalue is equally important to the user. Second, standard\nevaluation metrics focus on assessing the performance of the\nmodel on the most common cases. This package contains methods\nto tackle imbalanced domain learning problems in regression\ntasks, where the objective is to predict extreme (rare) values.\nThe methods contained in this package are: 1) an automatic and\nnon-parametric method to obtain such relevance functions; 2)\nvisualisation tools; 3) suite of evaluation measures for\noptimisation/validation processes; 4) the squared-error\nrelevance area measure, an evaluation metric tailored for\nimbalanced regression tasks. More information can be found in\nRibeiro and Moniz (2020) .", "URL": "https://github.com/nunompmoniz/IRon", "BugReports": "https://github.com/nunompmoniz/IRon/issues", "License": "CC0", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.2.1", "NeedsCompilation": "yes", "Config/pak/sysreqs": "cmake make libicu-dev", "Repository": "https://nunompmoniz.r-universe.dev", "Date/Publication": "2023-04-03 15:30:13 UTC", "RemoteUrl": "https://github.com/nunompmoniz/iron", "RemoteRef": "HEAD", "RemoteSha": "e7ad573698be7cada3065d24139d9768da1c5568", "Packaged": { "Date": "2026-06-01 11:38:58 UTC", "User": "root" }, "Author": "Nuno Moniz [cre, aut],\nRita P. 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