Lojistik regresyon modelinin kalitesini de\u011ferlendirmenin yayg\u0131n bir yolu, modelin tahmin edilen de\u011ferleri ile test veri k\u00fcmesinin ger\u00e7ek de\u011ferlerini g\u00f6steren 2×2’lik bir tablo olan bir kar\u0131\u015f\u0131kl\u0131k matrisi<\/strong> olu\u015fturmakt\u0131r.<\/span> <\/p>\n A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, Excel’de bir kar\u0131\u015f\u0131kl\u0131k matrisinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n \u0130lk olarak, bir yan\u0131t de\u011fi\u015fkeni i\u00e7in lojistik regresyon modelinin \u00f6ng\u00f6rd\u00fc\u011f\u00fc de\u011ferlerle birlikte ger\u00e7ek de\u011ferlerin yer ald\u0131\u011f\u0131 bir s\u00fctuna girelim:<\/span> <\/p>\n Daha sonra, Ger\u00e7ek s\u00fctununda “0” olan ve ayr\u0131ca Tahmin edilen s\u00fctununda “0” olan de\u011ferlerin say\u0131s\u0131n\u0131 saymak i\u00e7in COUNTIFS()<\/strong> form\u00fcl\u00fcn\u00fc kullanaca\u011f\u0131z:<\/span> <\/p>\n Kar\u0131\u015f\u0131kl\u0131k matrisindeki di\u011fer t\u00fcm h\u00fccreleri doldurmak i\u00e7in benzer bir form\u00fcl kullanaca\u011f\u0131z:<\/span> <\/p>\n Kar\u0131\u015f\u0131kl\u0131k matrisini olu\u015fturduktan sonra a\u015fa\u011f\u0131daki metrikleri hesaplayabiliriz:<\/span><\/p>\n A\u015fa\u011f\u0131daki form\u00fcller bu \u00f6l\u00e7\u00fcmlerin her birinin Excel’de nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span> <\/p>\n Kesinlik ne kadar y\u00fcksek olursa, model g\u00f6zlemleri o kadar do\u011fru \u015fekilde s\u0131n\u0131fland\u0131rabilir.<\/span><\/p>\n Bu \u00f6rnekte modelimizin do\u011frulu\u011fu 0,7’dir<\/strong> ve bu bize g\u00f6zlemlerin %70’ini do\u011fru \u015fekilde s\u0131n\u0131fland\u0131rd\u0131\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n \u0130stersek, g\u00f6zlemleri 0 veya 1 kategorilerine ay\u0131rmada hangi modelin en iyi oldu\u011funu belirlemek i\u00e7in bu do\u011frulu\u011fu di\u011fer lojistik regresyon modelleriyle kar\u015f\u0131la\u015ft\u0131rabiliriz.<\/span><\/p>\n Lojistik Regresyona Giri\u015f<\/a> Lojistik regresyon , yan\u0131t de\u011fi\u015fkeni ikili oldu\u011funda kullanabilece\u011fimiz bir regresyon t\u00fcr\u00fcd\u00fcr. Lojistik regresyon modelinin kalitesini de\u011ferlendirmenin yayg\u0131n bir yolu, modelin tahmin edilen de\u011ferleri ile test veri k\u00fcmesinin ger\u00e7ek de\u011ferlerini g\u00f6steren 2×2’lik bir tablo olan bir kar\u0131\u015f\u0131kl\u0131k matrisi olu\u015fturmakt\u0131r. A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, Excel’de bir kar\u0131\u015f\u0131kl\u0131k matrisinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6sterir. 1. Ad\u0131m: Verileri girin \u0130lk olarak, […]<\/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-2144","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/confusionr1.png\")
<\/p>\n 1. Ad\u0131m: Verileri girin<\/strong><\/span><\/h3>\n
![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/confexcel1.png\")
<\/p>\n Ad\u0131m 2: Kar\u0131\u015f\u0131kl\u0131k Matrisini Olu\u015fturun<\/strong><\/span><\/h3>\n
![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/confexcel2.png\")
<\/p>\n![\"Excel'deki](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/confexcel3.png\")
<\/p>\n 3. Ad\u0131m: Do\u011frulu\u011fu, Hassasiyeti Hesaplay\u0131n ve Geri \u00c7a\u011f\u0131rma<\/strong><\/span><\/h3>\n
\n
![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/confexcel4.png\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
3 t\u00fcr lojistik regresyon<\/a>
Lojistik regresyon ve do\u011frusal regresyon<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"