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  "Title": "Neyman-Pearson (NP) Classification Algorithms and NP Receiver\nOperating Characteristic (NP-ROC) Curves",
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  "Date": "2020-01-13",
  "Authors@R": "c(person(\"Yang\", \"Feng\", email = \"yangfengstat@gmail.com\",\nrole = c(\"aut\", \"cre\")),person(\"Jessica\", \"Li\",\nrole = c(\"aut\")),person(\"Xin\", \"Tong\",\nrole = c(\"aut\")),person(\"Ye\", \"Tian\", role=c(\"ctb\")))",
  "Description": "In many binary classification applications, such as\ndisease diagnosis and spam detection, practitioners commonly\nface the need to limit type I error (i.e., the conditional\nprobability of misclassifying a class 0 observation as class 1)\nso that it remains below a desired threshold. To address this\nneed, the Neyman-Pearson (NP) classification paradigm is a\nnatural choice; it minimizes type II error (i.e., the\nconditional probability of misclassifying a class 1 observation\nas class 0) while enforcing an upper bound, alpha, on the type\nI error. Although the NP paradigm has a century-long history in\nhypothesis testing, it has not been well recognized and\nimplemented in classification schemes. Common practices that\ndirectly limit the empirical type I error to no more than alpha\ndo not satisfy the type I error control objective because the\nresulting classifiers are still likely to have type I errors\nmuch larger than alpha. As a result, the NP paradigm has not\nbeen properly implemented for many classification scenarios in\npractice. In this work, we develop the first umbrella algorithm\nthat implements the NP paradigm for all scoring-type\nclassification methods, including popular methods such as\nlogistic regression, support vector machines and random\nforests. Powered by this umbrella algorithm, we propose a novel\ngraphical tool for NP classification methods: NP receiver\noperating characteristic (NP-ROC) bands, motivated by the\npopular receiver operating characteristic (ROC) curves. NP-ROC\nbands will help choose in a data adaptive way and compare\ndifferent NP classifiers.",
  "License": "GPL-2",
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  "URL": "http://advances.sciencemag.org/content/4/2/eaao1659",
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      "title": "Compare two NP classification methods at different type I error upper bounds.",
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      "title": "Add NP-ROC curves to the current plot object.",
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