{"id":6569,"date":"2025-12-01T13:37:06","date_gmt":"2025-12-01T05:37:06","guid":{"rendered":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6569"},"modified":"2025-12-01T13:37:35","modified_gmt":"2025-12-01T05:37:35","slug":"11-from-prediction-to-decision-ib533-2025-09","status":"publish","type":"page","link":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6569&lang=en","title":{"rendered":"11. From Prediction to Decision (IB533 2025.09)"},"content":{"rendered":"
Unit Outlines\uff1a<\/b><\/h5>\n
\u25c6 Linear Models Review<\/h6>\n
\u3002Linear Regression vs. Logistic Regression<\/h6>\n
\u3002Modeling and Predicting Process<\/h6>\n
\u3002Model Accuracy Measures<\/h6>\n
\u25c6 Model Accuracy of Logistic Regression<\/h6>\n
\u3002Predictions (Predicted Probability)<\/h6>\n
\u3002The Classified Distribution of Predicted Probability (DPP)<\/h6>\n
\u3002Threshold and Confusion Matrix<\/h6>\n
\u3002Accuracy, Sensitivity and Specificity<\/h6>\n
\u3002ROC and AUC<\/h6>\n
\u25c6 From Prediction to Decision<\/h6>\n
\u3002Optimal Strategy for Maximal Expected Payoff<\/h6>\n
\u3002Confusion Matrix and Payoff Matrix<\/h6>\n
\u3002Cutoff to Take Action<\/h6>\n
\u3002Simulation for Optimal Cutoff<\/h6>\n
\u3002Interactive Simulation in R<\/h6>\n
\n
Unit Notebook\uff1a<\/b><\/h5>\n
\u25c6 UNIT11A : Distribution of Predicted Probability ( unit11A.html<\/a> )<\/h6>\n
\u25c6 UNIT11B : Model Prediction vs. Business Decision ( unit11B.html<\/a> )<\/h6>\n
Simulation Codes\uff1a<\/b><\/h5>\n
\u25c6 Classified DPP Simulator( enDPPSIM.R )<\/h6>\n
\u25c6 Interactive Cutoff Simulator ( Sim11B.R )<\/h6>\n
\n
Google Drive\uff1a\u00a0<\/b><\/h5>\n
\u25c6 Course Material : [\u00a0 ~\/ib533\/<\/a> ]<\/h6>\n
\u25c6 Unit07:\u00a0 [ ~\/ib533\/11_Decision\/<\/a> ]<\/h6>\n
\n
Unit11 Assignment\uff1a Due: 2025\/12\/10 (Wed.) 23:59 <\/b><\/h5>\n
Personal Assignment<\/b>:<\/h6>\n
Finish the exercises in unit11A & unit11B, knit the html and<\/h6>\n
upload to folder [ ~\/person\/11_Decision\/<\/a> ]<\/h6>\n
with filename “<Student-ID>_unit11A.html”<\/h6>\n
with filename “<Student-ID>_unit11B.html”<\/h6>\n
\n
\u53c3\u8003\u9023\u7d50\uff1a<\/b><\/h6>\n
\u25c6\u00a0\u00a0From Data to Viz<\/a><\/h6>\n
\u25c6\u00a0\u00a0Portal of Big Data BA Platform<\/a><\/h6>\n
\u25c6\u00a0\u00a0R: Self Learning Roadmap<\/a><\/h6>\n
\u25c6\u00a0 John Hopkins: R Programming online course<\/a><\/h6>\n
\u25c6\u00a0\u00a0John Hopkins: R Programming eBook<\/a><\/h6>\n
\u25c6\u00a0\u00a0Harvard: Data Science online course<\/a><\/h6>\n
\u25c6\u00a0 Harvard: Data Science ebook<\/a><\/h6>\n
\n
\u25c6 Unit11 Video Recording<\/h6>\n
\n","protected":false},"excerpt":{"rendered":"

Unit Outlines\uff1a \u25c6 Linear Models Review \u3002Linear Regression vs. Logistic Regression \u3002Modeling and Predicting Process \u3002Model Accuracy Measures \u25c6 Model Accuracy of Logistic Regression \u3002Predictions (Predicted Probability) \u3002The Classified Distribution of Predicted Probability (DPP) \u3002Threshold and Confusion Matrix \u3002Accuracy, Sensitivity and Specificity \u3002ROC and AUC \u25c6 From Prediction to Decision \u3002Optimal Strategy for Maximal Expected Payoff \u3002Confusion Matrix and Payoff Matrix \u3002Cutoff to Take Action \u3002Simulation for Optimal Cutoff \u3002Interactive Simulation in R Unit Notebook\uff1a \u25c6 UNIT11A : Distribution of Predicted Probability ( unit11A.html ) \u25c6 UNIT11B : Model Prediction vs. Business Decision ( unit11B.html ) Simulation Codes\uff1a \u25c6 Classified DPP Simulator( enDPPSIM.R ) \u25c6 Interactive Cutoff Simulator ( Sim11B.R ) Google Drive\uff1a\u00a0 \u25c6 Course Material : [\u00a0 ~\/ib533\/ ] \u25c6 Unit07:\u00a0 [ ~\/ib533\/11_Decision\/ ] Unit11 Assignment\uff1a Due: 2025\/12\/10 (Wed.) 23:59 Personal Assignment: Finish the exercises in unit11A & unit11B, knit the html and upload to folder [ ~\/person\/11_Decision\/ ] with filename “<Student-ID>_unit11A.html” with filename “<Student-ID>_unit11B.html” \u53c3\u8003\u9023\u7d50\uff1a \u25c6\u00a0\u00a0From Data to Viz \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 Unit11 Video Recording<\/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-6569","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6569","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=6569"}],"version-history":[{"count":2,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6569\/revisions"}],"predecessor-version":[{"id":6571,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6569\/revisions\/6571"}],"wp:attachment":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}