{"id":2118,"date":"2023-07-23T15:05:59","date_gmt":"2023-07-23T15:05:59","guid":{"rendered":"https:\/\/statorials.org\/tr\/ridge-lasso-regresyonu-ne-zaman-kullanilir\/"},"modified":"2023-07-23T15:05:59","modified_gmt":"2023-07-23T15:05:59","slug":"ridge-lasso-regresyonu-ne-zaman-kullanilir","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/ridge-lasso-regresyonu-ne-zaman-kullanilir\/","title":{"rendered":"Ridge &amp; lasso regresyon ne zaman kullan\u0131l\u0131r?"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">S\u0131radan <a href=\"https:\/\/statorials.org\/tr\/coklu-dogrusal-regresyon\/\" target=\"_blank\" rel=\"noopener\">\u00e7oklu do\u011frusal regresyonda<\/a> ,<\/span> <span style=\"color: #000000;\">formun bir modeline uyacak \u015fekilde bir dizi <em>p<\/em> tahmin de\u011fi\u015fkeni ve bir yan\u0131t de\u011fi\u015fkeni kullan\u0131r\u0131z:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Y = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> X <sub>1<\/sub> <sub>+<\/sub> \u03b2 <sub>2<\/sub> X <sub>2<\/sub> + \u2026 + \u03b2 <sub>p<\/sub><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u03b2 <sub>0<\/sub> , \u03b2 <sub>1<\/sub> , B <sub>2<\/sub> , \u2026, \u03b2 <sub>p<\/sub> de\u011ferleri, art\u0131klar\u0131n karelerinin toplam\u0131n\u0131 (RSS) en aza indiren en k\u00fc\u00e7\u00fck kareler y\u00f6ntemi kullan\u0131larak se\u00e7ilir:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS = \u03a3(y <sub>ben<\/sub> \u2013 \u0177 <sub>ben<\/sub> ) <sup>2<\/sup><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Alt\u0131n:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> : \u201cToplam\u201d anlam\u0131na gelen bir sembol<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> : <sup>i&#8217;inci<\/sup> g\u00f6zlem i\u00e7in ger\u00e7ek yan\u0131t de\u011feri<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> : <sup>i&#8217;inci<\/sup> g\u00f6zlem i\u00e7in tahmin edilen yan\u0131t de\u011feri<\/span><\/li>\n<\/ul>\n<h3> <strong><span style=\"color: #000000;\">Regresyonda \u00e7oklu ba\u011flant\u0131 sorunu<\/span><\/strong><\/h3>\n<p> <span style=\"color: #000000;\">\u00c7oklu do\u011frusal regresyonda pratikte s\u0131kl\u0131kla ortaya \u00e7\u0131kan bir sorun, <a href=\"https:\/\/statorials.org\/tr\/coklu-baglanti-regresyonu\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u00e7oklu do\u011frusall\u0131kt\u0131r<\/a> ; yani iki veya daha fazla yorday\u0131c\u0131 de\u011fi\u015fken birbiriyle y\u00fcksek d\u00fczeyde korelasyona sahip oldu\u011funda, regresyon modelinde benzersiz veya ba\u011f\u0131ms\u0131z bilgi sa\u011flayamazlar.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu, model katsay\u0131 tahminlerini g\u00fcvenilmez hale getirebilir ve y\u00fcksek varyans sergileyebilir. Yani model daha \u00f6nce hi\u00e7 g\u00f6rmedi\u011fi yeni bir veri setine uyguland\u0131\u011f\u0131nda muhtemelen d\u00fc\u015f\u00fck performans g\u00f6sterecektir.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u00c7oklu ba\u011flant\u0131dan ka\u00e7\u0131nma: Ridge &amp; Lasso regresyonu<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Bu \u00e7oklu ba\u011flant\u0131 problemini a\u015fmak i\u00e7in kullanabilece\u011fimiz iki y\u00f6ntem <strong>ridge regresyonu<\/strong> ve <strong>kement regresyonudur<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>Ridge regresyonu<\/strong> a\u015fa\u011f\u0131dakileri en aza indirmeyi ama\u00e7lamaktad\u0131r:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>RSS + \u03bb\u03a3\u03b2 <sub>j<\/sub> <sup>2<\/sup><\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>Kement regresyonu<\/strong> a\u015fa\u011f\u0131dakileri en aza indirmeyi ama\u00e7lar:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>RSS + \u03bb\u03a3|\u03b2 <sub>j<\/sub> |<\/strong><\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Her iki denklemde de ikinci terime <em>\u00e7ekilme cezas\u0131<\/em> denir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u03bb = 0 oldu\u011funda, bu ceza teriminin hi\u00e7bir etkisi yoktur ve s\u0131rt regresyonu ve kement regresyonu, en k\u00fc\u00e7\u00fck kareler ile ayn\u0131 katsay\u0131 tahminlerini \u00fcretir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ancak \u03bb sonsuza yakla\u015ft\u0131k\u00e7a b\u00fcz\u00fclme cezas\u0131 daha etkili hale gelir ve modele aktar\u0131lamayan tahmin de\u011fi\u015fkenleri s\u0131f\u0131ra do\u011fru azal\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Lasso regresyonunda \u03bb yeterince b\u00fcy\u00fck oldu\u011funda baz\u0131 katsay\u0131lar\u0131n <em>tamamen s\u0131f\u0131r<\/em> olmas\u0131 m\u00fcmk\u00fcnd\u00fcr.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ridge &amp; Lasso Regresyonunun Avantajlar\u0131 ve Dezavantajlar\u0131<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Ridge ve Lasso regresyonunun en k\u00fc\u00e7\u00fck kareler regresyonuna g\u00f6re <strong>avantaj\u0131<\/strong> <a href=\"https:\/\/statorials.org\/tr\/onyargi-varyansi-uzlasmasi\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u00f6nyarg\u0131-varyans de\u011fi\u015f toku\u015fudur<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ortalama Karesel Hatan\u0131n (MSE) belirli bir modelin do\u011frulu\u011funu \u00f6l\u00e7mek i\u00e7in kullanabilece\u011fimiz bir \u00f6l\u00e7\u00fcm oldu\u011funu ve \u015fu \u015fekilde hesapland\u0131\u011f\u0131n\u0131 hat\u0131rlay\u0131n:<\/span><\/p>\n<p> <span style=\"color: #000000;\">MSE = Var( <em class=\"ph i\">f\u0302(<\/em> x <sub>0<\/sub> )) + [\u00d6nyarg\u0131( <em class=\"ph i\">f\u0302(<\/em> x <sub>0<\/sub> ))] <sup>2<\/sup> + Var(\u03b5)<\/span><\/p>\n<p> <span style=\"color: #000000;\">MSE = Varyans + \u00d6nyarg\u0131 <sup>2<\/sup> + \u0130ndirgenemez hata<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ridge Regression ve Lasso Regression&#8217;\u0131n temel fikri, varyans\u0131n \u00f6nemli \u00f6l\u00e7\u00fcde azalt\u0131labilmesi ve b\u00f6ylece daha d\u00fc\u015f\u00fck bir genel MSE&#8217;ye yol a\u00e7abilmesi i\u00e7in k\u00fc\u00e7\u00fck bir \u00f6nyarg\u0131 eklemektir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bunu a\u00e7\u0131klamak i\u00e7in a\u015fa\u011f\u0131daki grafi\u011fi inceleyin:<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11851 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/crete1.png\" alt=\"Ridge Regresyon \u00d6nyarg\u0131-Varyans Dengesi\" width=\"468\" height=\"341\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\u03bb artt\u0131k\u00e7a sapmadaki \u00e7ok k\u00fc\u00e7\u00fck bir art\u0131\u015fla varyans\u0131n \u00f6nemli \u00f6l\u00e7\u00fcde azald\u0131\u011f\u0131n\u0131 unutmay\u0131n. Ancak belirli bir noktadan sonra varyans daha yava\u015f azal\u0131r ve katsay\u0131lardaki azalma onlar\u0131n \u00f6nemli \u00f6l\u00e7\u00fcde eksik tahmin edilmesine yol a\u00e7ar, bu da yanl\u0131l\u0131\u011f\u0131n keskin bir \u015fekilde artmas\u0131na neden olur.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Grafikten, \u03bb i\u00e7in \u00f6nyarg\u0131 ve varyans aras\u0131nda optimal bir denge sa\u011flayan bir de\u011fer se\u00e7ti\u011fimizde testin MSE&#8217;sinin en d\u00fc\u015f\u00fck oldu\u011funu g\u00f6rebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u03bb = 0 oldu\u011funda, kement regresyonundaki ceza teriminin hi\u00e7bir etkisi yoktur ve bu nedenle en k\u00fc\u00e7\u00fck kareler ile ayn\u0131 katsay\u0131 tahminlerini \u00fcretir.<\/span> <span style=\"color: #000000;\">Ancak \u03bb&#8217;y\u0131 belirli bir noktaya art\u0131rarak testin genel MSE&#8217;sini azaltabiliriz.<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11874 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/lasso1.png\" alt=\"Kement Regresyon \u00d6nyarg\u0131-Varyans Dengesi\" width=\"490\" height=\"357\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">Bu, s\u0131rt ve kement regresyonuyla model uydurman\u0131n, en k\u00fc\u00e7\u00fck kareler regresyonuyla model uydurmaya g\u00f6re potansiyel olarak daha k\u00fc\u00e7\u00fck test hatalar\u0131 \u00fcretebilece\u011fi anlam\u0131na gelir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ridge ve Lasso regresyonunun <strong>dezavantaj\u0131<\/strong> , katsay\u0131lar\u0131n s\u0131f\u0131ra yakla\u015ft\u0131k\u00e7a son modelde yorumlanmas\u0131n\u0131n zorla\u015fmas\u0131d\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu nedenle, \u00e7\u0131kar\u0131m yerine tahmin yetene\u011fini optimize etmek istedi\u011finizde Ridge ve Lasso regresyonu kullan\u0131lmal\u0131d\u0131r.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ridge vs. Kement Regresyon: Her Biri Ne Zaman Kullan\u0131lmal\u0131<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">L asso regresyonu ve ridge regresyonu <em>d\u00fczenlile\u015ftirme y\u00f6ntemleri<\/em> olarak bilinir, \u00e7\u00fcnk\u00fc her ikisi de art\u0131k kareler toplam\u0131n\u0131 (RSS) ve belirli bir ceza terimini en aza indirmeye \u00e7al\u0131\u015f\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ba\u015fka bir deyi\u015fle, model katsay\u0131lar\u0131n\u0131n tahminlerini k\u0131s\u0131tlar veya <em>d\u00fczenlerler<\/em> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu do\u011fal olarak \u015fu soruyu g\u00fcndeme getiriyor: <strong>S\u0131rt regresyonu mu yoksa kement regresyonu mu daha iyi?<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Yaln\u0131zca az say\u0131da \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenin anlaml\u0131 oldu\u011fu durumlarda, <strong>kement regresyonu<\/strong> daha iyi \u00e7al\u0131\u015fma e\u011filimindedir \u00e7\u00fcnk\u00fc \u00f6nemsiz de\u011fi\u015fkenleri tamamen s\u0131f\u0131ra indirebilir ve bunlar\u0131 modelden \u00e7\u0131karabilir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bununla birlikte, bir\u00e7ok yorday\u0131c\u0131 de\u011fi\u015fken modelde anlaml\u0131 oldu\u011funda ve katsay\u0131lar\u0131 yakla\u015f\u0131k olarak e\u015fit oldu\u011funda, <strong>ridge regresyonu<\/strong> daha iyi \u00e7al\u0131\u015fma e\u011filimindedir \u00e7\u00fcnk\u00fc t\u00fcm yorday\u0131c\u0131lar\u0131 modelde tutar.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tahmin yapmak i\u00e7in hangi modelin en iyi oldu\u011funu belirlemek i\u00e7in genellikle <a href=\"https:\/\/statorials.org\/tr\/k-kat-capraz-dogrulama\/\" target=\"_blank\" rel=\"noopener noreferrer\">k-katl\u0131 \u00e7apraz do\u011frulama<\/a> ger\u00e7ekle\u015ftiririz ve en d\u00fc\u015f\u00fck test k\u00f6k ortalama kare hatas\u0131n\u0131 \u00fcreten modeli se\u00e7eriz.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ek kaynaklar<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki e\u011fitimler Ridge Regression ve Lasso Regression&#8217;a giri\u015f sa\u011flar:<\/span><\/p>\n<ul>\n<li> <a href=\"https:\/\/statorials.org\/tr\/sirtin-gerilemesi\/\" target=\"_blank\" rel=\"noopener\">Ridge Regresyonuna Giri\u015f<\/a><\/li>\n<li> <a href=\"https:\/\/statorials.org\/tr\/kement-regresyonu\/\" target=\"_blank\" rel=\"noopener\">Kement Regresyonuna Giri\u015f<\/a><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki e\u011fitimlerde R ve Python&#8217;da her iki regresyon t\u00fcr\u00fcn\u00fcn nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n<ul>\n<li> <a href=\"https:\/\/statorials.org\/tr\/rde-tepe-regresyonu\/\" target=\"_blank\" rel=\"noopener\">R&#8217;de Ridge regresyonu<\/a><\/li>\n<li> <a href=\"https:\/\/statorials.org\/tr\/pythonda-kret-regresyonu\/\" target=\"_blank\" rel=\"noopener\">Python&#8217;da Ridge Regresyon<\/a><\/li>\n<li> <a href=\"https:\/\/statorials.org\/tr\/rde-kement-regresyonu\/\" target=\"_blank\" rel=\"noopener\">R&#8217;de Kement Regresyon<\/a><\/li>\n<li> <a href=\"https:\/\/statorials.org\/tr\/pythonda-kement-regresyonu\/\" target=\"_blank\" rel=\"noopener\">Python&#8217;da Kement Regresyon<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>S\u0131radan \u00e7oklu do\u011frusal regresyonda , formun bir modeline uyacak \u015fekilde bir dizi p tahmin de\u011fi\u015fkeni ve bir yan\u0131t de\u011fi\u015fkeni kullan\u0131r\u0131z: Y = \u03b2 0 + \u03b2 1 X 1 + \u03b2 2 X 2 + \u2026 + \u03b2 p \u03b2 0 , \u03b2 1 , B 2 , \u2026, \u03b2 p de\u011ferleri, art\u0131klar\u0131n karelerinin toplam\u0131n\u0131 [&hellip;]<\/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-2118","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Ridge &amp; Lasso Regression Ne Zaman Kullan\u0131l\u0131r - Statoryaller<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde \u00f6rneklerle s\u0131rt regresyonu ve kement regresyonunun ne zaman kullan\u0131laca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/tr\/ridge-lasso-regresyonu-ne-zaman-kullanilir\/\" \/>\n<meta property=\"og:locale\" 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