{"id":6551,"date":"2025-11-17T16:33:23","date_gmt":"2025-11-17T08:33:23","guid":{"rendered":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6551"},"modified":"2025-11-20T23:46:31","modified_gmt":"2025-11-20T15:46:31","slug":"10-%e9%82%8f%e8%bc%af%e5%bc%8f%e8%bf%b4%e6%ad%b8-cm512-2025-09","status":"publish","type":"page","link":"https:\/\/bap2.cm.nsysu.edu.tw\/?page_id=6551","title":{"rendered":"10. \u908f\u8f2f\u5f0f\u8ff4\u6b78 (CM512 2025.09)"},"content":{"rendered":"
\u7b2c\u5341\u55ae\u5143 \u55ae\u5143\u5927\u7db1\uff1a<\/b><\/h5>\n
\u25a0 \u908f\u8f2f\u5f0f\u56de\u6b78\u7c21\u4ecb<\/h5>\n
\u00a7 \u908f\u8f2f\u5f0f\u51fd\u6578 Logistic Function<\/h5>\n
\u00a7 \u6a5f\u7387\u3001\u52dd\u7387\u3001Logit<\/h5>\n
\u25a0 \u7528R\u505a\u908f\u8f2f\u5f0f\u56de\u6b78<\/h5>\n
\u00a7 \u6a5f\u7387\u3001\u52dd\u7387\u3001Logit<\/h5>\n
\u00a7 \u908f\u8f2f\u5f0f\u51fd\u6578 Logistic Function<\/h5>\n
\u00a7 \u5efa\u7acb\u6a21\u578b glm(\u2026 , family=binomial)<\/h5>\n
\u00a7 \u9810\u6e2c\u6a5f\u7387 predict(model, data, type=”response”)<\/h5>\n
\u25a0 \u6a21\u578b\u4fc2\u6578\u5224\u8b80<\/h5>\n
\u00a7 exp(\u8ff4\u6b78\u4fc2\u6578) = \u52dd\u7387\u500d\u6578<\/h5>\n
\u00a7 \u908a\u969b\u6548\u679c\u76f8\u4e58<\/h5>\n
\u25a0 \u985e\u5225\u6a21\u578b\u7684\u6e96\u78ba\u6027\u6307\u6a19<\/h5>\n
\u00a7 \u6df7\u6dc6\u77e9\u9663 Confusion Matrix<\/h5>\n
\u00a7 \u6e96\u78ba\u6027 Accuracy<\/h5>\n
\u00a7 \u654f\u611f\u6027 Sensitivity<\/h5>\n
\u00a7 \u660e\u78ba\u6027 Specificity<\/h5>\n
\u00a7 \u8fa8\u8b58\u7387 AUC<\/h5>\n
\u25a0 \u6a21\u64ec\u6848\u4f8b\uff1a\u9810\u9632\u6027\u91ab\u7642<\/h5>\n
\n
\u7b2c\u5341\u55ae\u5143 \u8ab2\u5802\u8207\u4f5c\u696d\u7b46\u8a18<\/b>\uff1a<\/b><\/h5>\n
\u25c6 \u8ab2\u5802\u7b46\u8a18 UNIT10A\uff1a\u908f\u8f2f\u5f0f\u56de\u6b78\uff1a\u7c21\u4ecb Logistic Regression ( unit10A.html<\/a> )<\/h5>\n
\u25c6 \u8ab2\u5802\u7b46\u8a18 UNIT10B\uff1a\u908f\u8f2f\u5f0f\u56de\u6b78\uff1a\u4fc2\u6578\u5224\u8b80 Regression Coefficients ( unit10B.html<\/a> )<\/h5>\n
\u25c6 \u8ab2\u5802\u7b46\u8a18 UNIT10C\uff1a\u61c9\u7528\u6848\u4f8b\uff1a\u9810\u9632\u6027\u91ab\u7642 Predictive Modeling ( unit10C.html<\/a> )<\/h5>\n
\u4e92\u52d5\u6a21\u64ec\u7a0b\u5f0f<\/b>\uff1a<\/b><\/h5>\n
\u25c6 Shiny App \uff1a\u300c\u5206\u985e\u9810\u6e2c\u6a5f\u7387\u5206\u4f48\u300d\u6a21\u64ec\u5668 ( DPPSIM.R )<\/h5>\n
\n
\u7b2c\u5341\u55ae\u5143 \u6559\u6750\u4e0b\u8f09\uff1a<\/b><\/h5>\n
\u25c6 \u6559\u6750\u8cc7\u6599\u593e ( ~\/cm512\/<\/a> )<\/h6>\n
\u25c6 Unit10\u55ae\u5143\u8cc7\u6599\u593e ( ~\/cm512\/10\u908f\u8f2f\u5f0f\u8ff4\u6b78\/<\/a> )<\/h6>\n
\u00a010\u908f\u8f2f\u5f0f\u8ff4\u6b78.pptx<\/h6>\n
\u00a0unit10_LogisticReg.zip<\/h6>\n
\n
\u7b2c\u5341\u55ae \u500b\u4eba\u4f5c\u696d \uff1a \u671f\u9650 11\/27 (\u56db) 23:59<\/b><\/h5>\n
\u505a\u597d`unit10B|C.Rmd`\u88e1\u9762\u7684\u7df4\u7fd2\uff0cKnit\u6210HTML\u6a94\uff1a”\u5b78\u865f_10B|C.html”<\/h5>\n
\u5c07\u4ee5\u4e0a\u6a94\u6848\u4e0a\u50b3\u5230\u3010 ~\/\u500b\u4eba\u4f5c\u696d\/10_Logistic<\/a> \u3011<\/h5>\n
\n
\u5f71\u7247\u9023\u7d50\uff1a<\/b><\/h5>\n
\u25c6 \u7b2c\u5341\u55ae\u5143 \u4e0a\u8ab2\u9304\u5f71<\/h5>\n
\n
\u53c3\u8003\u9023\u7d50\uff1a<\/b><\/h5>\n
\u25c6\u00a0\u00a0\u4e2d\u5c71\u7ba1\u9662\u5927\u6578\u64da\u5e73\u53f0 \u5165\u53e3\u7db2\u7ad9<\/a><\/h5>\n
\u25c6\u00a0\u00a0R\uff1a\u8cc7\u6599\u5206\u6790\u8207\u57fa\u790e\u7d71\u8a08 \u7dda\u4e0a\u8ab2\u7a0b\u81ea\u5b78\u5730\u5716<\/a><\/h5>\n
\u25c6\u00a0 John Hopkins: R Programming \u7dda\u4e0a\u8ab2\u7a0b<\/a><\/h5>\n
\u25c6\u00a0\u00a0John Hopkins: R Programming eBook<\/a><\/h5>\n
\u25c6\u00a0\u00a0Harvard: Data Science \u7dda\u4e0a\u8ab2\u7a0b<\/a><\/h5>\n
\u25c6\u00a0 Harvard: Data Science \u8ab2\u5802\u7b46\u8a18<\/a><\/h5>\n
\u25c6\u00a0\u00a0Probability Cheat Sheet (pdf<\/a>)<\/h5>\n
\u25c6\u00a0Intro. Probability and Statistics Using R (IPSUR<\/a>)<\/h5>\n
\u25c6\u00a0Statistical Analysis Using R, UCLA (URL<\/a>)<\/h5>\n
\u25c6\u00a0CLUSTER ANALYSIS IN R: PRACTICAL GUIDE (URL<\/a>)<\/h5>\n
\u25c6\u00a0Principal Component Methods in R: Practical Guide (URL<\/a>)<\/h5>\n","protected":false},"excerpt":{"rendered":"

\u7b2c\u5341\u55ae\u5143 \u55ae\u5143\u5927\u7db1\uff1a \u25a0 \u908f\u8f2f\u5f0f\u56de\u6b78\u7c21\u4ecb \u00a7 \u908f\u8f2f\u5f0f\u51fd\u6578 Logistic Function \u00a7 \u6a5f\u7387\u3001\u52dd\u7387\u3001Logit \u25a0 \u7528R\u505a\u908f\u8f2f\u5f0f\u56de\u6b78 \u00a7 \u6a5f\u7387\u3001\u52dd\u7387\u3001Logit \u00a7 \u908f\u8f2f\u5f0f\u51fd\u6578 Logistic Function \u00a7 \u5efa\u7acb\u6a21\u578b glm(\u2026 , family=binomial) \u00a7 \u9810\u6e2c\u6a5f\u7387 predict(model, data, type=”response”) \u25a0 \u6a21\u578b\u4fc2\u6578\u5224\u8b80 \u00a7 exp(\u8ff4\u6b78\u4fc2\u6578) = \u52dd\u7387\u500d\u6578 \u00a7 \u908a\u969b\u6548\u679c\u76f8\u4e58 \u25a0 \u985e\u5225\u6a21\u578b\u7684\u6e96\u78ba\u6027\u6307\u6a19 \u00a7 \u6df7\u6dc6\u77e9\u9663 Confusion Matrix \u00a7 \u6e96\u78ba\u6027 Accuracy \u00a7 \u654f\u611f\u6027 Sensitivity \u00a7 \u660e\u78ba\u6027 Specificity \u00a7 \u8fa8\u8b58\u7387 AUC \u25a0 \u6a21\u64ec\u6848\u4f8b\uff1a\u9810\u9632\u6027\u91ab\u7642 \u7b2c\u5341\u55ae\u5143 \u8ab2\u5802\u8207\u4f5c\u696d\u7b46\u8a18\uff1a \u25c6 \u8ab2\u5802\u7b46\u8a18 UNIT10A\uff1a\u908f\u8f2f\u5f0f\u56de\u6b78\uff1a\u7c21\u4ecb Logistic Regression ( unit10A.html ) \u25c6 \u8ab2\u5802\u7b46\u8a18 UNIT10B\uff1a\u908f\u8f2f\u5f0f\u56de\u6b78\uff1a\u4fc2\u6578\u5224\u8b80 Regression Coefficients ( unit10B.html ) \u25c6 \u8ab2\u5802\u7b46\u8a18 UNIT10C\uff1a\u61c9\u7528\u6848\u4f8b\uff1a\u9810\u9632\u6027\u91ab\u7642 Predictive Modeling ( unit10C.html ) \u4e92\u52d5\u6a21\u64ec\u7a0b\u5f0f\uff1a \u25c6 Shiny App \uff1a\u300c\u5206\u985e\u9810\u6e2c\u6a5f\u7387\u5206\u4f48\u300d\u6a21\u64ec\u5668 ( DPPSIM.R ) \u7b2c\u5341\u55ae\u5143 \u6559\u6750\u4e0b\u8f09\uff1a \u25c6 \u6559\u6750\u8cc7\u6599\u593e ( ~\/cm512\/ ) \u25c6 Unit10\u55ae\u5143\u8cc7\u6599\u593e ( ~\/cm512\/10\u908f\u8f2f\u5f0f\u8ff4\u6b78\/ ) \u00a010\u908f\u8f2f\u5f0f\u8ff4\u6b78.pptx \u00a0unit10_LogisticReg.zip \u7b2c\u5341\u55ae \u500b\u4eba\u4f5c\u696d \uff1a \u671f\u9650 11\/27 (\u56db) 23:59 \u505a\u597d`unit10B|C.Rmd`\u88e1\u9762\u7684\u7df4\u7fd2\uff0cKnit\u6210HTML\u6a94\uff1a”\u5b78\u865f_10B|C.html” \u5c07\u4ee5\u4e0a\u6a94\u6848\u4e0a\u50b3\u5230\u3010 ~\/\u500b\u4eba\u4f5c\u696d\/10_Logistic \u3011 \u5f71\u7247\u9023\u7d50\uff1a \u25c6 \u7b2c\u5341\u55ae\u5143 \u4e0a\u8ab2\u9304\u5f71 \u53c3\u8003\u9023\u7d50\uff1a \u25c6\u00a0\u00a0\u4e2d\u5c71\u7ba1\u9662\u5927\u6578\u64da\u5e73\u53f0 \u5165\u53e3\u7db2\u7ad9 \u25c6\u00a0\u00a0R\uff1a\u8cc7\u6599\u5206\u6790\u8207\u57fa\u790e\u7d71\u8a08 \u7dda\u4e0a\u8ab2\u7a0b\u81ea\u5b78\u5730\u5716 \u25c6\u00a0 John Hopkins: R Programming \u7dda\u4e0a\u8ab2\u7a0b \u25c6\u00a0\u00a0John Hopkins: R Programming eBook \u25c6\u00a0\u00a0Harvard: Data Science \u7dda\u4e0a\u8ab2\u7a0b \u25c6\u00a0 Harvard: Data Science \u8ab2\u5802\u7b46\u8a18 \u25c6\u00a0\u00a0Probability Cheat Sheet (pdf) \u25c6\u00a0Intro. Probability and Statistics Using R (IPSUR) \u25c6\u00a0Statistical Analysis Using R, UCLA (URL) \u25c6\u00a0CLUSTER ANALYSIS IN R: PRACTICAL GUIDE (URL) \u25c6\u00a0Principal 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-6551","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6551","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=6551"}],"version-history":[{"count":5,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6551\/revisions"}],"predecessor-version":[{"id":6560,"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=\/wp\/v2\/pages\/6551\/revisions\/6560"}],"wp:attachment":[{"href":"https:\/\/bap2.cm.nsysu.edu.tw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6551"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}