{ "_id": "6a214a9fcd65a98ecbd2c417", "Package": "wconf", "Type": "Package", "Title": "Weighted Confusion Matrix", "Version": "1.2.0", "Authors@R": "person(given = \"Alexandru\",\nfamily = \"Monahov\",\nrole = c(\"aut\", \"cre\", \"cph\"),\nemail = \"alexandru.monahov@proton.me\",\ncomment = c(ORCID = \"0000-0001-6204-9131\"))", "URL": "https://www.alexandrumonahov.eu.org/projects", "Description": "Allows users to create weighted confusion matrices and\naccuracy metrics that help with the model selection process for\nclassification problems, where distance from the correct\ncategory is important. The package includes several weighting\nschemes which can be parameterized, as well as custom\nconfiguration options. Furthermore, users can decide whether\nthey wish to positively or negatively affect the accuracy score\nas a result of applying weights to the confusion matrix.\nFunctions are included to calculate accuracy metrics for\nimbalanced data. Finally, 'wconf' integrates well with the\n'caret' package, but it can also work standalone when provided\ndata in matrix form. References: Kuhn, M. (2008) \"Building\nPerspective Models in R Using the caret Package\"\n Monahov, A. (2021) \"Model\nEvaluation with Weighted Threshold Optimization (and the mewto\nR package)\" Monahov, A. (2024)\n\"Improved Accuracy Metrics for Classification with Imbalanced\nData and Where Distance from the Truth Matters, with the wconf\nR Package\" Starovoitov, V., Golub,\nY. (2020). New Function for Estimating Imbalanced Data\nClassification Results. Pattern Recognition and Image Analysis,\n295–302 Van de Velden, M., Iodice D'Enza, A., Markos, A.,\nCavicchia, C. 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