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  "Package": "qCBA",
  "Title": "Postprocessing of Rule Classification Models Learnt on Quantized\nData",
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  "Date": "2025-04-03",
  "Maintainer": "Tomáš Kliegr <kliegr@gmail.com>",
  "Description": "Implements the Quantitative Classification-based on\nAssociation Rules (QCBA) algorithm\n(<doi:10.1007/s10489-022-04370-x>). QCBA postprocesses rule\nclassification models making them typically smaller and in some\ncases more accurate. Supported are 'CBA' implementations from\n'rCBA', 'arulesCBA' and 'arc' packages, and 'CPAR', 'CMAR',\n'FOIL2' and 'PRM' implementations from 'arulesCBA' package and\n'SBRL' implementation from the 'sbrl' package. The result of\nthe post-processing is an ordered CBA-like rule list.",
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  "BugReports": "https://github.com/kliegr/QCBA/issues",
  "License": "GPL-3",
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  "Repository": "https://kliegr.r-universe.dev",
  "Date/Publication": "2025-04-08 11:20:11 UTC",
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  "Author": "Tomáš Kliegr [aut, cre]",
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    "getConfVectorForROC",
    "mapDataTypes",
    "qcba",
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    "qcbaIris",
    "qcbaIris2",
    "rcbaModel2CBARuleModel",
    "sbrlModel2arcCBARuleModel",
    "show"
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      "title": "arulesCBA2arcCBAModel Converts a model created by 'arulesCBA' so that it can be passed to qCBA",
      "topics": [
        "arulesCBA2arcCBAModel"
      ]
    },
    {
      "page": "benchmarkQCBA",
      "title": "Learn and evaluate QCBA postprocessing on multiple rule learners.  This can be, for example, used to automatically select the best model for a given use case based on a combined preference for accuracy and model size.",
      "topics": [
        "benchmarkQCBA"
      ]
    },
    {
      "page": "customCBARuleModel-class",
      "title": "customCBARuleModel",
      "topics": [
        "customCBARuleModel",
        "customCBARuleModel-class"
      ]
    },
    {
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      "title": "Returns vector with confidences for the positive class (useful for ROC or AUC computation)",
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        "getConfVectorForROC"
      ]
    },
    {
      "page": "mapDataTypes",
      "title": "Map R types to qCBA",
      "topics": [
        "mapDataTypes"
      ]
    },
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      "title": "Aplies qCBARuleModel",
      "topics": [
        "predict.qCBARuleModel"
      ]
    },
    {
      "page": "qcba",
      "title": "qCBA Quantitative CBA",
      "topics": [
        "qcba"
      ]
    },
    {
      "page": "qcbaHumTemp",
      "title": "Use the HumTemp dataset to test the one rule classification QCBA workflow.",
      "topics": [
        "qcbaHumTemp"
      ]
    },
    {
      "page": "qcbaIris",
      "title": "Use the iris dataset to the test QCBA workflow.",
      "topics": [
        "qcbaIris"
      ]
    },
    {
      "page": "qcbaIris2",
      "title": "Use the Iris dataset to test the experimental multi-rule QCBA workflow.",
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        "qcbaIris2"
      ]
    },
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      "page": "qCBARuleModel-class",
      "title": "QCBA Rule Model",
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        "qCBARuleModel",
        "qCBARuleModel-class",
        "show,qCBARuleModel-method"
      ]
    },
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      "topics": [
        "rcbaModel2CBARuleModel"
      ]
    },
    {
      "page": "sbrlModel2arcCBARuleModel",
      "title": "sbrlModel2arcCBARuleModel Converts a model created by 'sbrl' so that it can be passed to qCBA",
      "topics": [
        "sbrlModel2arcCBARuleModel"
      ]
    }
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