<\/p>\n
R’de kar\u015f\u0131la\u015fabilece\u011finiz yayg\u0131n bir uyar\u0131:<\/span><\/p>\n Bu uyar\u0131 genellikle R’ye bir lojistik regresyon modeli s\u0131\u011fd\u0131rmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ve m\u00fckemmel ay\u0131rmay\u0131<\/strong> g\u00f6rd\u00fc\u011f\u00fcn\u00fczde, yani bir yorday\u0131c\u0131 de\u011fi\u015fkenin yan\u0131t de\u011fi\u015fkenini 0 ve 1’e m\u00fckemmel \u015fekilde ay\u0131rabildi\u011fini g\u00f6rd\u00fc\u011f\u00fcn\u00fczde ortaya \u00e7\u0131kar.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu uyar\u0131n\u0131n pratikte nas\u0131l ele al\u0131naca\u011f\u0131 g\u00f6sterilmektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki lojistik regresyon modelini R’ye s\u0131\u011fd\u0131rmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n \u015eu uyar\u0131 mesaj\u0131n\u0131 ald\u0131\u011f\u0131m\u0131z\u0131 unutmay\u0131n: glm.fit: algoritma yak\u0131nsamad\u0131<\/strong> .<\/span><\/p>\n Bu mesaj\u0131 al\u0131yoruz \u00e7\u00fcnk\u00fc tahmin de\u011fi\u015fkeni x, yan\u0131t de\u011fi\u015fkeni y’yi 0 ve 1’e m\u00fckemmel bir \u015fekilde ay\u0131rabiliyor.<\/span><\/p>\n 1’den k\u00fc\u00e7\u00fck her x de\u011feri i\u00e7in y’nin 0’a e\u015fit oldu\u011funu ve 1’e e\u015fit veya 1’den b\u00fcy\u00fck her x de\u011feri i\u00e7in y’nin 1’e e\u015fit oldu\u011funu unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, tahmin de\u011fi\u015fkeninin yan\u0131t de\u011fi\u015fkenini 0’lara ve 1’lere m\u00fckemmel \u015fekilde ay\u0131ramad\u0131\u011f\u0131 bir senaryoyu g\u00f6sterir:<\/span><\/p>\n Tahmin de\u011fi\u015fkeni yan\u0131t de\u011fi\u015fkenini 0 ve 1’e tam olarak ay\u0131ramad\u0131\u011f\u0131ndan herhangi bir uyar\u0131 mesaj\u0131 alm\u0131yoruz.<\/span><\/p>\n M\u00fckemmel bir ayr\u0131l\u0131k senaryosuyla kar\u015f\u0131la\u015f\u0131rsak bunu halletmenin iki yolu vard\u0131r:<\/span><\/p>\n Y\u00f6ntem 1: Cezaland\u0131r\u0131lm\u0131\u015f regresyon kullan\u0131n.<\/strong><\/span><\/p>\n Se\u00e7eneklerden biri, kement lojistik regresyonu veya elastik a\u011f d\u00fczenlemesi gibi cezaland\u0131r\u0131lm\u0131\u015f bir lojistik regresyon bi\u00e7iminin kullan\u0131lmas\u0131d\u0131r.<\/span><\/p>\n R’de cezaland\u0131r\u0131lm\u0131\u015f lojistik regresyonun nas\u0131l uygulanaca\u011f\u0131na ili\u015fkin se\u00e7enekler i\u00e7in glmnet<\/a> paketine bak\u0131n.<\/span><\/p>\n Y\u00f6ntem 2: Yan\u0131t de\u011fi\u015fkenini m\u00fckemmel \u015fekilde tahmin etmek i\u00e7in yorday\u0131c\u0131 de\u011fi\u015fkeni kullan\u0131n.<\/strong><\/span><\/p>\n Pop\u00fclasyonda bu m\u00fckemmel ayr\u0131m\u0131n mevcut olabilece\u011finden \u015f\u00fcpheleniyorsan\u0131z, yan\u0131t de\u011fi\u015fkeninin de\u011ferini m\u00fckemmel bir \u015fekilde tahmin etmek i\u00e7in bu yorday\u0131c\u0131 de\u011fi\u015fkeni kullanabilirsiniz.<\/span><\/p>\n \u00d6rne\u011fin yukar\u0131daki senaryoda, yorday\u0131c\u0131 de\u011fi\u015fken x<\/strong> 1’den k\u00fc\u00e7\u00fck oldu\u011funda yan\u0131t de\u011fi\u015fkeni y’nin<\/strong> her zaman 0’a e\u015fit oldu\u011funu g\u00f6rd\u00fck.<\/span><\/p>\n Bu ili\u015fkinin genel pop\u00fclasyonda ge\u00e7erli oldu\u011fundan \u015f\u00fcphelenirsek, x<\/strong> 1’den k\u00fc\u00e7\u00fck oldu\u011funda y’nin<\/strong> de\u011ferinin 0 olaca\u011f\u0131n\u0131 her zaman tahmin edebiliriz ve cezaland\u0131r\u0131lm\u0131\u015f bir lojistik regresyon modeline uyma konusunda endi\u015felenmeyiz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler, R’de glm()<\/strong> i\u015flevinin kullan\u0131m\u0131na ili\u015fkin ek bilgiler sa\u011flar:<\/span><\/p>\n R’de glm ve lm aras\u0131ndaki fark<\/a> R’de kar\u015f\u0131la\u015fabilece\u011finiz yayg\u0131n bir uyar\u0131: glm.fit: algorithm did not converge Bu uyar\u0131 genellikle R’ye bir lojistik regresyon modeli s\u0131\u011fd\u0131rmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ve m\u00fckemmel ay\u0131rmay\u0131 g\u00f6rd\u00fc\u011f\u00fcn\u00fczde, yani bir yorday\u0131c\u0131 de\u011fi\u015fkenin yan\u0131t de\u011fi\u015fkenini 0 ve 1’e m\u00fckemmel \u015fekilde ay\u0131rabildi\u011fini g\u00f6rd\u00fc\u011f\u00fcn\u00fczde ortaya \u00e7\u0131kar. A\u015fa\u011f\u0131daki \u00f6rnekte bu uyar\u0131n\u0131n pratikte nas\u0131l ele al\u0131naca\u011f\u0131 g\u00f6sterilmektedir. Uyar\u0131 nas\u0131l yeniden olu\u015fturulur? A\u015fa\u011f\u0131daki lojistik regresyon […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-2256","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n glm.fit: algorithm did not converge\n<\/strong><\/span><\/pre>\n Uyar\u0131 nas\u0131l yeniden olu\u015fturulur?<\/strong><\/span><\/h3>\n
#create data frame<\/span>\ndf <- data. frame<\/span> (x=c(.1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1, 1.1, 1.3, 1.5, 1.7),\n y=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1))\n\n#attempt to fit logistic regression model\n<\/span>glm(y~x, data=df, family=\" binomial<\/span> \")\n\nCall: glm(formula = y ~ x, family = \"binomial\", data = df)\n\nCoefficients:\n(Intercept)x \n -409.1 431.1 \n\nDegrees of Freedom: 14 Total (ie Null); 13 Residual\nNull Deviance: 20.19 \nResidual Deviance: 2.468e-09 AIC: 4\nWarning messages:\n1: glm.fit: algorithm did not converge \n2: glm.fit: fitted probabilities numerically 0 or 1 occurred \n<\/span><\/span><\/strong><\/pre>\n #create data frame\n<\/span>df <- data. frame<\/span> (x=c(.1, .2, .3, .4, .5, .6, .7, .8, .9, 1, 1, 1.1, 1.3, 1.5, 1.7),\n y=c(0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1))\n\n#fit logistic regression model\n<\/span>glm(y~x, data=df, family=\" binomial<\/span> \")\n\nCall: glm(formula = y ~ x, family = \"binomial\", data = df)\n\nCoefficients:\n(Intercept) x \n -2.112 2.886 \n\nDegrees of Freedom: 14 Total (ie Null); 13 Residual\nNull Deviance: 20.73 \nResidual Deviance: 16.31 AIC: 20.31\n<\/span><\/span><\/strong><\/pre>\n Uyar\u0131 nas\u0131l ele al\u0131n\u0131r?<\/strong><\/span><\/h3>\n
Ek kaynaklar<\/strong><\/span><\/h3>\n
R’de glm ile tahmin i\u015flevi nas\u0131l kullan\u0131l\u0131r?<\/a>
Nas\u0131l ele al\u0131n\u0131r: glm.fit: say\u0131sal olarak ayarlanm\u0131\u015f olas\u0131l\u0131klar 0 veya 1’in olu\u015fmas\u0131<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"