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  "Title": "Evaluation Metrics for Customer Scoring Models Depending on\nBinary Classifiers",
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  "Author": "Koen W. De Bock",
  "Maintainer": "Koen W. De Bock <kdebock@audencia.com>",
  "Description": "Functions for evaluating and visualizing predictive model\nperformance (specifically: binary classifiers) in the field of\ncustomer scoring. These metrics include lift, lift index, gain\npercentage, top-decile lift, F1-score, expected\nmisclassification cost and absolute misclassification cost. See\nBerry & Linoff (2004, ISBN:0-471-47064-3), Witten and Frank\n(2005, 0-12-088407-0) and Blattberg, Kim & Neslin (2008,\nISBN:978–0–387–72578–9) for details. Visualization functions\nare included for lift charts and gain percentage charts. All\nmetrics that require class predictions offer the possibility to\ndynamically determine cutoff values for transforming\nreal-valued probability predictions into class predictions.",
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  "Encoding": "UTF-8",
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  "Date/Publication": "2018-04-06 09:39:01 UTC",
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      "page": "checkDepVector",
      "title": "Perform check on the true class label vector",
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      "title": "Calculate lift index",
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    },
    {
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      "title": "Calculate top-decile lift",
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