{"id":1734,"date":"2016-07-12T12:07:18","date_gmt":"2016-07-12T12:07:18","guid":{"rendered":"http:\/\/sites.uac.pt\/amendes\/?p=1734"},"modified":"2016-07-12T12:07:18","modified_gmt":"2016-07-12T12:07:18","slug":"roc-analysis","status":"publish","type":"post","link":"https:\/\/sites.uac.pt\/amendes\/data-mining\/roc-analysis\/","title":{"rendered":"The Many Faces of ROC Analysis"},"content":{"rendered":"<div id=\"attachment_1733\" style=\"width: 310px\" class=\"wp-caption aligncenter\"><a href=\"http:\/\/www.cs.bris.ac.uk\/~flach\/ICML04tutorial\/\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-1733\" class=\"size-medium wp-image-1733\" src=\"http:\/\/sites.uac.pt\/amendes\/files\/2016\/07\/iclm-top-300x43.jpg\" alt=\"clicar na imagem para seguir o link\" width=\"300\" height=\"43\" srcset=\"https:\/\/sites.uac.pt\/amendes\/files\/2016\/07\/iclm-top-300x43.jpg 300w, https:\/\/sites.uac.pt\/amendes\/files\/2016\/07\/iclm-top.jpg 744w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><p id=\"caption-attachment-1733\" class=\"wp-caption-text\">clicar na imagem para seguir o link<\/p><\/div>\n<p><span style=\"color: #ff0000\">Bom tutorial sobre curvas ROC<\/span><\/p>\n<p>Receiver Operating Characteristics (ROC) Analysis originated from signal detection theory, as a model of how well a receiver is able to detect a signal in the presence of noise. Its key feature is the distinction between <em>hit rate<\/em> (or <em>true positive rate<\/em>) and <em>false alarm rate<\/em> (or <em>false positive rate<\/em>) as two separate performance measures. ROC analysis has also widely been used in medical data analysis to study the effect of varying the threshold on the numerical outcome of a diagnostic test. It has been introduced to machine learning relatively recently, in response to classification tasks with varying class distributions or misclassification costs (hereafter referred to as <em>skew<\/em>).  ROC analysis is set to cause a paradigm shift in machine learning. Separating performance on classes is almost always a good idea from an analytical perspective. For instance, it can help us to<\/p>\n<ul type=\"square\">\n<li>understand the behaviour and skew-sensitivity of many machine      learning metrics, including rule learning heuristics and decision tree      splitting criteria, by plotting their isometrics in ROC space;<\/li>\n<li>develop new metrics specifically designed to improve the Area      Under the ROC Curve (AUC) of a model;<\/li>\n<li>understand fundamental algorithms such as the      separate-and-conquer or sequential covering rule learning algorithm, by      tracing its trajectory through a sequence of ROC spaces.<\/li>\n<\/ul>\n<p>The goal of this tutorial is to develop the ROC perspective in a systematic way, demonstrating the many faces of ROC analysis in machine learning.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Bom tutorial sobre curvas ROC Receiver Operating Characteristics (ROC) Analysis originated from signal detection theory, as a model of how well a receiver is able to detect a signal in the presence of noise. Its key feature is the distinction between hit rate (or true positive rate) and false alarm rate (or false positive rate) [&hellip;]<\/p>\n","protected":false},"author":159,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[103,150],"tags":[171,191],"class_list":["post-1734","post","type-post","status-publish","format-standard","hentry","category-data-mining","category-materiais-para-profissionais","tag-dw-bi","tag-machine-learning"],"_links":{"self":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1734","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/users\/159"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/comments?post=1734"}],"version-history":[{"count":2,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1734\/revisions"}],"predecessor-version":[{"id":1736,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/posts\/1734\/revisions\/1736"}],"wp:attachment":[{"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/media?parent=1734"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/categories?post=1734"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.uac.pt\/amendes\/wp-json\/wp\/v2\/tags?post=1734"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}