{"id":6825,"date":"2026-11-04T13:47:32","date_gmt":"2026-11-04T05:47:32","guid":{"rendered":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6825"},"modified":"2026-11-04T13:47:32","modified_gmt":"2026-11-04T05:47:32","slug":"10-logistic-regression-ib533-2026-09","status":"publish","type":"page","link":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6825&lang=en","title":{"rendered":"10. Logistic Regression (IB533 2026.09)"},"content":{"rendered":"
Unit Outlines\uff1a<\/b><\/span><\/h5>\n
\u25a0 Logistic Regression<\/span><\/h5>\n
\u00a7 method\uff1aglm(\u2026 , family = binomial)<\/span><\/h5>\n
\u00a7 model fitting & prediction<\/span><\/h5>\n
\u00a7 interpreting the coefficients<\/span><\/h5>\n
\u25a0 Measures of Model Accuracy<\/span><\/h5>\n
\u00a7 Accuracy, Sensibility, Specificity<\/span><\/h5>\n
\u00a7 Confusion Matrix and Accuracy Metrics<\/span><\/h5>\n
\u00a7 ROC and AUC<\/span><\/h5>\n
\n
Notebooks<\/b>\uff1a<\/b><\/span><\/h5>\n
\u25c6 UNIT10A\uff1a Logistic Regression (unit10A.html<\/a>)<\/span><\/h5>\n
\u25c6 UNIT10B\uff1aLogisticReg. – The Coefficients (unit10B.html<\/a>)<\/span><\/h5>\n
\u25c6 UNIT10C LogisticReg. – Model Accuracy (unit10C.html<\/a>)<\/span><\/h5>\n
\n
Google Drive\uff1a\u00a0<\/b><\/span><\/h5>\n
\u25c6 Unit10:\u00a0 [ ~\/ib533\/10_LogisticReg\/<\/a> ]<\/span><\/h6>\n
\n
Unit10\u00a0 Personal Assignments \uff1a Due at\u00a0 2025\/11\/23 (Mon.) 23:59<\/b><\/span><\/h5>\n
Trace the code and answer the quiz in uni10A|B.Rmd,<\/span><\/h6>\n
knit and upload the HTML files to<\/span><\/h6>\n
upload it to \u3010 ~\/person\/10_LogisticReg<\/a> \u3011with filename “<student.id>_unit10A|B.html”<\/span><\/h6>\n
\n
Video Recording\uff1a<\/b><\/span><\/h5>\n
\u25c6 Unit10 Video Recording<\/span><\/h5>\n
\n
References :<\/b><\/span><\/h5>\n
\u25c6\u00a0\u00a0Portal of Big Data BA Platform<\/a><\/span><\/h6>\n
\u25c6\u00a0\u00a0R: Self Learning Roadmap<\/a><\/span><\/h6>\n
\u25c6\u00a0 John Hopkins: R Programming online course<\/a><\/span><\/h6>\n
\u25c6\u00a0\u00a0John Hopkins: R Programming eBook<\/a><\/span><\/h6>\n
\u25c6\u00a0\u00a0Harvard: Data Science online course<\/a><\/span><\/h6>\n
\u25c6\u00a0 Harvard: Data Science ebook<\/a><\/span><\/h6>\n
\u25c6\u00a0\u00a0From Data to Viz<\/a><\/span><\/h6>\n
\u25c6\u00a0\u00a0Probability Cheat Sheet (pdf<\/a>)<\/span><\/h5>\n
\u25c6\u00a0 Intro. Probability and Statistics Using R (IPSUR<\/a>)<\/span><\/h5>\n
\u25c6\u00a0 Statistical Analysis Using R, UCLA (URL<\/a>)<\/span><\/h5>\n
\u25c6\u00a0 CLUSTER ANALYSIS IN R: PRACTICAL GUIDE (URL<\/a>)<\/span><\/h5>\n
\u25c6\u00a0 Principal Component Methods in R: Practical Guide (URL<\/a>)<\/span><\/h5>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

Unit Outlines\uff1a \u25a0 Logistic Regression \u00a7 method\uff1aglm(\u2026 , family = binomial) \u00a7 model fitting & prediction \u00a7 interpreting the coefficients \u25a0 Measures of Model Accuracy \u00a7 Accuracy, Sensibility, Specificity \u00a7 Confusion Matrix and Accuracy Metrics \u00a7 ROC and AUC Notebooks\uff1a \u25c6 UNIT10A\uff1a Logistic Regression (unit10A.html) \u25c6 UNIT10B\uff1aLogisticReg. – The Coefficients (unit10B.html) \u25c6 UNIT10C LogisticReg. – Model Accuracy (unit10C.html) Google Drive\uff1a\u00a0 \u25c6 Unit10:\u00a0 [ ~\/ib533\/10_LogisticReg\/ ] Unit10\u00a0 Personal Assignments \uff1a Due at\u00a0 2025\/11\/23 (Mon.) 23:59 Trace the code and answer the quiz in uni10A|B.Rmd, knit and upload the HTML files to upload it to \u3010 ~\/person\/10_LogisticReg \u3011with filename “<student.id>_unit10A|B.html” Video Recording\uff1a \u25c6 Unit10 Video Recording References : \u25c6\u00a0\u00a0Portal of Big Data BA Platform \u25c6\u00a0\u00a0R: Self Learning Roadmap \u25c6\u00a0 John Hopkins: R Programming online course \u25c6\u00a0\u00a0John Hopkins: R Programming eBook \u25c6\u00a0\u00a0Harvard: Data Science online course \u25c6\u00a0 Harvard: Data Science ebook \u25c6\u00a0\u00a0From Data to Viz \u25c6\u00a0\u00a0Probability Cheat Sheet (pdf) \u25c6\u00a0 Intro. Probability and Statistics Using R (IPSUR) \u25c6\u00a0 Statistical Analysis Using R, UCLA (URL) \u25c6\u00a0 CLUSTER ANALYSIS IN R: PRACTICAL GUIDE (URL) \u25c6\u00a0 Principal Component Methods in R: Practical Guide (URL)  <\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"class_list":["post-6825","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6825","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6825"}],"version-history":[{"count":1,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6825\/revisions"}],"predecessor-version":[{"id":6826,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6825\/revisions\/6826"}],"wp:attachment":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6825"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}