{"id":4068,"date":"2023-12-14T08:09:29","date_gmt":"2023-12-14T08:09:29","guid":{"rendered":"https:\/\/bulutistan.com\/blog\/?p=4068"},"modified":"2024-01-20T10:23:42","modified_gmt":"2024-01-20T10:23:42","slug":"denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis","status":"publish","type":"post","link":"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/","title":{"rendered":"Denetimli ve G\u00f6zetimli \u00d6\u011frenme Nedir? Supervised Learning Genel Bak\u0131\u015f"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme, modellerin do\u011fru tahminler yapmas\u0131na yard\u0131mc\u0131 olmak i\u00e7in etiketli e\u011fitim verilerini kullanan makine \u00f6\u011freniminin en yayg\u0131n uygulanan dallar\u0131ndan biridir. Buradaki e\u011fitim verileri makineler i\u00e7in bir g\u00f6zetmen ve \u00f6\u011fretmen g\u00f6revi g\u00f6r\u00fcr, dolay\u0131s\u0131yla ad\u0131 da buradan gelir. Benzer bir metodoloji, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, spam filtreleme, risk de\u011ferlendirmesi, doland\u0131r\u0131c\u0131l\u0131k tespiti gibi ger\u00e7ek d\u00fcnyadaki zorluklar\u0131n \u00e7\u00f6z\u00fclmesinde etkilidir.<\/span><\/p>\n<h2 id=\"makine-ogrenimi-nedir\"><b>Makine \u00d6\u011frenimi Nedir?<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Makine \u00f6\u011frenimi, bilgisayarlar\u0131n a\u00e7\u0131k\u00e7a programlanmadan deneyimlerden \u00f6\u011frenmesini ve geli\u015fmesini sa\u011flayan bir yapay zeka alt k\u00fcmesidir. Bir programc\u0131n\u0131n belirli bir g\u00f6revi yerine getirmek i\u00e7in kod yazd\u0131\u011f\u0131 geleneksel programlaman\u0131n aksine makine \u00f6\u011freniminde sistem, verileri analiz etmek ve zaman i\u00e7inde performans\u0131n\u0131 art\u0131rmak i\u00e7in istatistiksel algoritmalar kullan\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Makine \u00f6\u011frenimi algoritmalar\u0131 verilerdeki kal\u0131plar\u0131 ve ili\u015fkileri tan\u0131mlayabilir, tahminlerde bulunabilir ve bu verilere dayanarak kararlar alabilir. Bu yakla\u015f\u0131m, \u00f6nceden belirlenmi\u015f kurallara, algoritmalara ve sezgisel y\u00f6ntemlere dayanan ve yeni verilere veya de\u011fi\u015fen ko\u015fullara uyum sa\u011flamayan geleneksel bilgisayar programlamas\u0131ndan farkl\u0131d\u0131r. Bilgisayarlara verilen basit g\u00f6revler i\u00e7in, makineye eldeki sorunu \u00e7\u00f6zmek i\u00e7in gereken t\u00fcm ad\u0131mlar\u0131 nas\u0131l y\u00fcr\u00fctece\u011fini s\u00f6yleyen algoritmalar programlamak m\u00fcmk\u00fcnd\u00fcr; bilgisayar a\u00e7\u0131s\u0131ndan \u00f6\u011frenmeye gerek yoktur. Daha geli\u015fmi\u015f g\u00f6revler i\u00e7in bir insan\u0131n gerekli algoritmalar\u0131 manuel olarak olu\u015fturmas\u0131 zor olabilir. Makine \u00f6\u011frenimi programlar\u0131, a\u00e7\u0131k\u00e7a programlanmadan da g\u00f6revleri yerine getirebilir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bulutistan hizmetlerinin detaylar\u0131na ula\u015fmak i\u00e7in <\/span><a href=\"https:\/\/bulutistan.com\/cloud\/\"><span style=\"font-weight: 400;\">t\u0131klay\u0131n\u0131z<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"makine-ogrenimi-modellerinin-turleri\"><b>Makine \u00d6\u011frenimi Modellerinin T\u00fcrleri<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Makine \u00f6\u011frenimi algoritmalar\u0131 ama\u00e7lar\u0131na ve benzerliklerine g\u00f6re grupland\u0131r\u0131l\u0131r. Kategorileri tan\u0131mlamak s\u00f6z konusu oldu\u011funda g\u00f6r\u00fc\u015fler ayr\u0131l\u0131r, ancak genel olarak d\u00f6rt t\u00fcr makine \u00f6\u011frenimi t\u00fcr\u00fc vard\u0131r:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimsiz \u00f6\u011frenme<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Yar\u0131 denetimli \u00f6\u011frenme<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Peki\u015ftirmeli \u00f6\u011frenme<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">K\u0131saca, denetimli \u00f6\u011frenmenin tahmin problemleri i\u00e7in denetimsiz \u00f6\u011frenmenin verilerin yap\u0131s\u0131n\u0131 anlamak i\u00e7in ve peki\u015ftirmeli \u00f6\u011frenmenin karma\u015f\u0131k durumlarda karar vermek i\u00e7in kullan\u0131ld\u0131\u011f\u0131n\u0131 s\u00f6yleyebiliriz.<\/span><\/p>\n<h2 id=\"denetimli-ogrenme-nedir\"><b>Denetimli \u00d6\u011frenme Nedir?<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Makine \u00f6\u011frenimine y\u00f6nelik k\u00fcresel pazar\u0131n 2024 y\u0131l\u0131na kadar %42 y\u0131ll\u0131k bile\u015fik b\u00fcy\u00fcme oran\u0131 (CAGR) ile geni\u015flemesi beklendi\u011finden, temel bir makine \u00f6\u011frenimi metodolojisi olarak denetimli \u00f6\u011frenme her zamankinden daha \u00f6nemli hale gelmektedir. Hedef de\u011fi\u015fken i\u00e7in istenen sonu\u00e7lar\u0131 elde etmek \u00fczere verileri eyleme ge\u00e7irilebilir i\u00e7g\u00f6r\u00fclere d\u00f6n\u00fc\u015ft\u00fcrme yetene\u011fi, giderek artan say\u0131da sekt\u00f6re fayda sa\u011flamaktad\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme, denetimli makine \u00f6\u011frenimi ile ayn\u0131 \u015fekilde, veri geli\u015ftirmeye ve ge\u00e7mi\u015f deneyimlerden (etiketli veriler) bir \u00e7\u0131kt\u0131 \u00fcretmeye dayan\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bu, girdi verilerinin etiketli \u00f6rneklerden olu\u015ftu\u011fu anlam\u0131na gelir: her veri noktas\u0131 bir veri \u00f6rne\u011fi (girdi nesnesi) ve hedef etiket (tahmin edilmek istenen) \u00e7iftidir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenmede, bir girdi de\u011fi\u015fkeni bir makine \u00f6\u011frenimi modeli taraf\u0131ndan \u00f6\u011frenilen bir e\u015fleme fonksiyonu yard\u0131m\u0131yla bir \u00e7\u0131kt\u0131 de\u011fi\u015fkenine e\u015flenir. Denetimli bir \u00f6\u011frenme algoritmas\u0131 e\u011fitim verilerini analiz eder ve yeni \u00f6rnekleri e\u015flemek i\u00e7in kullan\u0131labilecek bir \u00e7\u0131kar\u0131m i\u015flevi \u00fcretir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bu, \u00f6\u011frenme algoritmas\u0131n\u0131n e\u011fitim verilerinden g\u00f6r\u00fclmeyen durumlara &#8220;makul&#8221; bir \u015fekilde genelleme yapmas\u0131n\u0131 gerektirir. Bir algoritman\u0131n bu istatistiksel kalitesi, genelleme hatas\u0131 olarak adland\u0131r\u0131lan de\u011ferle \u00f6l\u00e7\u00fcl\u00fcr. Test verilerinin amac\u0131, etiketlenmemi\u015f veri k\u00fcmeleri \u00fczerindeki genelleme hatas\u0131n\u0131 tahmin etmektir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Elbette t\u00fcm bunlar, makine \u00f6\u011frenimi modeline kaliteli e\u011fitim verileri sa\u011fland\u0131\u011f\u0131nda m\u00fcmk\u00fcnd\u00fcr. \u0130kincisi, model performans\u0131nda ciddi iyile\u015fmelere yol a\u00e7arak size rakiplerinize kar\u015f\u0131 \u00f6nemli bir avantaj sa\u011flayabilir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme modelinin e\u011fitim verilerini biriktirme ve performans kriterlerini kullanma becerisi \u00f6nceki deneyimlerden kaynakland\u0131\u011f\u0131ndan, ayn\u0131 veriler gelecekteki olaylar\u0131 tahmin etmek ve mevcut e\u011fitim verilerini iyile\u015ftirmek i\u00e7in kullan\u0131l\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bir anlamda, denetimli \u00f6\u011frenme s\u00fcreci etiketli e\u011fitim verilerinin toplanmas\u0131 ve haz\u0131rlanmas\u0131yla ba\u015flar ve bu veriler bir kez topland\u0131ktan sonra etiketli veriler farkl\u0131 gruplara\/versiyonlara ayr\u0131l\u0131r.<\/span><\/p>\n<h2 id=\"denetimli-ogrenme-algoritmalari\"><b>Denetimli \u00d6\u011frenme Algoritmalar\u0131<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme algoritmalar\u0131n\u0131n amac\u0131, kullan\u0131c\u0131n\u0131n nihai sonuca ula\u015fmas\u0131na yard\u0131mc\u0131 olmak i\u00e7in hangi ad\u0131mlar\u0131n at\u0131lmas\u0131 gerekti\u011fini anlamakt\u0131r. Denetimli \u00f6\u011frenme temel olarak regresyon ve s\u0131n\u0131fland\u0131rma olmak \u00fczere iki genel sorun t\u00fcr\u00fcn\u00fc ele ald\u0131\u011f\u0131ndan, bir dizi farkl\u0131 denetimli \u00f6\u011frenme modeli t\u00fcr\u00fc vard\u0131r. En yayg\u0131n olanlardan baz\u0131lar\u0131 a\u015fa\u011f\u0131daki \u015fekildedir:<\/span><\/p>\n<h3 id=\"dogrusal-regresyon\"><b>Do\u011frusal regresyon<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u00c7o\u011fu durumda, do\u011frusal regresyon hem makine \u00f6\u011freniminde hem de istatistikte en pop\u00fcler ve basit algoritmalardan biri olarak kabul edilir. Temel olarak gelecekteki sonu\u00e7lar\u0131 tahmin etmek i\u00e7in kullan\u0131lan do\u011frusal regresyon denetimli \u00f6\u011frenme algoritmas\u0131, herhangi bir de\u011fi\u015fken aras\u0131ndaki ba\u011flant\u0131y\u0131 a\u00e7\u0131klamak i\u00e7in e\u011fimli bir d\u00fcz \u00e7izgi sunarak ba\u011f\u0131ml\u0131 bir de\u011fi\u015fken ile bir veya daha fazla di\u011fer ba\u011f\u0131ms\u0131z de\u011fi\u015fken aras\u0131ndaki ba\u011flant\u0131y\u0131 tan\u0131mlamak i\u00e7in kullan\u0131l\u0131r. Basitle\u015ftirmek gerekirse, do\u011frusal regresyon tahminsel analiz i\u00e7in kullan\u0131lan istatistiksel bir prosed\u00fcrd\u00fcr; sat\u0131\u015flar\u0131, \u00fcr\u00fcn fiyatland\u0131rmas\u0131n\u0131, ya\u015f\u0131 vb. tahmin etmek i\u00e7in kullan\u0131l\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Tek bir ba\u011f\u0131ms\u0131z de\u011fi\u015fken ve tek bir ba\u011f\u0131ml\u0131 de\u011fi\u015fken oldu\u011funda, basit do\u011frusal regresyon olarak adland\u0131r\u0131l\u0131r ve ba\u011f\u0131ms\u0131z de\u011fi\u015fkenler eklendi\u011finde, s\u00fcre\u00e7 \u00e7oklu do\u011frusal regresyon haline gelir.<\/span><\/p>\n<h3 id=\"lojistik-regresyon\"><b>Lojistik regresyon<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Do\u011frusal regresyona benzer \u015fekilde lojistik regresyon modelleri de veri girdileri aras\u0131ndaki ili\u015fkileri tan\u0131maya \u00e7al\u0131\u015f\u0131r. Lojistik regresyon temel olarak spam tan\u0131mlama dahil olmak \u00fczere ikili s\u0131n\u0131fland\u0131rma sorunlar\u0131n\u0131 \u00e7\u00f6zmek i\u00e7in kullan\u0131l\u0131r ve ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin evet ve hay\u0131r veya do\u011fru ve yanl\u0131\u015f gibi ikili \u00e7\u0131kt\u0131lara sahip oldu\u011fu durumlarda yayg\u0131n olarak kullan\u0131l\u0131r. S\u00fcrekli ve ayr\u0131k veri k\u00fcmelerine at\u0131fta bulunarak olas\u0131l\u0131klar\u0131 bulma ve yeni verileri kategorize etme yetene\u011fi nedeniyle uygun s\u0131n\u0131fland\u0131rma algoritmalar\u0131ndan biri olarak kabul edilir.<\/span><\/p>\n<h3 id=\"destek-vektor-makinesi\"><b>Destek vekt\u00f6r makinesi<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Destek vekt\u00f6r makinesi hem veri regresyonu hem de s\u0131n\u0131fland\u0131rma i\u00e7in kullan\u0131l\u0131r, ancak \u00e7o\u011funlukla s\u0131n\u0131fland\u0131rma sorunlar\u0131n\u0131 \u00e7\u00f6zmek i\u00e7in kullan\u0131l\u0131r. S\u0131n\u0131fland\u0131rma sorunlar\u0131yla kar\u015f\u0131la\u015f\u0131ld\u0131\u011f\u0131nda, bu denetimli \u00f6\u011frenme algoritmas\u0131 karar s\u0131n\u0131r\u0131 olarak da bilinen bir hiper d\u00fczlem olu\u015fturur; d\u00fczlemin her iki taraf\u0131ndaki iki veri noktas\u0131 s\u0131n\u0131f\u0131n\u0131 ay\u0131r\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Destek vekt\u00f6r makinesi, destek vekt\u00f6rleri olarak da bilinen u\u00e7 vekt\u00f6rleri se\u00e7er ve bunlar hiper d\u00fczlemin olu\u015fturulmas\u0131na yard\u0131mc\u0131 olur. Do\u011frusal olarak ayr\u0131labilen veriler i\u00e7in kullan\u0131lan do\u011frusal destek vekt\u00f6r makinesi ve do\u011frusal olarak ayr\u0131lamayan verilerle \u00e7al\u0131\u015f\u0131rken ba\u015fvurulan do\u011frusal olmayan destek vekt\u00f6r makinesi olmak \u00fczere iki t\u00fcr destek vekt\u00f6r makinesi vard\u0131r.<\/span><\/p>\n<h3 id=\"sinir-aglari\"><b>Sinir a\u011flar\u0131<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Sinir a\u011flar\u0131, insan beyninin yap\u0131s\u0131na ve i\u015flevine benzeyen bir t\u00fcr makine \u00f6\u011frenimi algoritmas\u0131d\u0131r. Bilgileri i\u015fleyen ve tahminlerde bulunan birbirine ba\u011fl\u0131 d\u00fc\u011f\u00fcmlerden veya yapay n\u00f6ronlardan olu\u015furlar.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sinir a\u011flar\u0131, g\u00f6r\u00fcnt\u00fc ve konu\u015fma tan\u0131ma, do\u011fal dil i\u015fleme ve karar verme dahil olmak \u00fczere \u00e7ok \u00e7e\u015fitli g\u00f6revler i\u00e7in kullan\u0131labilir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Sinir a\u011flar\u0131n\u0131n en g\u00fc\u00e7l\u00fc y\u00f6nlerinden biri, a\u011f\u0131n giri\u015f verilerine g\u00f6re a\u011f\u0131rl\u0131klar\u0131n\u0131 ve \u00f6nyarg\u0131lar\u0131n\u0131 ayarlad\u0131\u011f\u0131 bir e\u011fitim s\u00fcreci arac\u0131l\u0131\u011f\u0131yla zaman i\u00e7inde \u00f6\u011frenme ve iyile\u015ftirme yetenekleridir. Bu, sinir a\u011flar\u0131n\u0131n verilerdeki karma\u015f\u0131k, do\u011frusal olmayan ili\u015fkileri ele almas\u0131na ve do\u011fru tahminler yapmas\u0131na olanak tan\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bununla birlikte, sinir a\u011flar\u0131n\u0131n tasarlanmas\u0131 ve e\u011fitilmesi zaman al\u0131c\u0131 ve hesaplama a\u00e7\u0131s\u0131ndan zor bir s\u00fcre\u00e7 olabilir ve mimari, aktivasyon fonksiyonlar\u0131 ve optimizasyon algoritmalar\u0131n\u0131n se\u00e7imi performanslar\u0131n\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde etkileyebilir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Derin \u00f6\u011frenme, \u00e7ok katmanl\u0131 sinir a\u011flar\u0131 geli\u015ftirmeyi i\u00e7eren makine \u00f6\u011freniminin bir alt alan\u0131d\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">&#8220;Derin&#8221; olarak adland\u0131r\u0131l\u0131r \u00e7\u00fcnk\u00fc giri\u015f ve \u00e7\u0131k\u0131\u015f katmanlar\u0131 aras\u0131nda verilerin hiyerar\u015fik temsilini \u00f6\u011frenmeye yard\u0131mc\u0131 olan gizli katmanlar vard\u0131r. Bu, derin \u00f6\u011frenme algoritmalar\u0131n\u0131, verilerin genellikle karma\u015f\u0131k bir yap\u0131ya sahip oldu\u011fu ve \u00fcst d\u00fczey \u00f6zelliklerin alt d\u00fczey \u00f6zelliklerden \u00f6\u011frenilebildi\u011fi g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, konu\u015fma tan\u0131ma ve do\u011fal dil i\u015fleme gibi g\u00f6revler i\u00e7in uygun hale getirir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Derin \u00f6\u011frenme algoritmalar\u0131 bir\u00e7ok alanda son teknoloji \u00fcr\u00fcn\u00fc sonu\u00e7lar elde etmeyi ba\u015farm\u0131\u015ft\u0131r ve \u015fu anda s\u00fcr\u00fcc\u00fcs\u00fcz arabalardan t\u0131bbi te\u015fhise kadar geni\u015f bir uygulama yelpazesinde kullan\u0131lmaktad\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Derin \u00f6\u011frenme, veri bilimcilerin model olu\u015ftururken dikkate almas\u0131 gereken zorluklarla da kar\u015f\u0131 kar\u015f\u0131yad\u0131r. En b\u00fcy\u00fck zorluk, genellikle y\u00fcksek maliyetli olan b\u00fcy\u00fck e\u011fitim verilerine ve bilgi i\u015flem kaynaklar\u0131na ihtiya\u00e7 duyulmas\u0131d\u0131r. Ayr\u0131ca, son derece karma\u015f\u0131k modeller e\u011fitilirken genellikle a\u015f\u0131r\u0131 uyum riski vard\u0131r. Bununla birlikte, donan\u0131m ve algoritmalardaki geli\u015fmeler, derin sinir a\u011flar\u0131n\u0131n devasa veri k\u00fcmeleri \u00fczerinde e\u011fitilmesini m\u00fcmk\u00fcn k\u0131larak derin \u00f6\u011frenmede s\u00fcrekli b\u00fcy\u00fcmeye ve ba\u015far\u0131ya yol a\u00e7m\u0131\u015ft\u0131r.<\/span><\/p>\n<h3 id=\"karar-agaclari\"><b>Karar a\u011fa\u00e7lar\u0131<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Karar a\u011fac\u0131, hem regresyon hem de s\u0131n\u0131fland\u0131rma problemlerini \u00e7\u00f6zmek i\u00e7in kullan\u0131lan en pop\u00fcler denetimli makine \u00f6\u011frenimi algoritmalar\u0131ndan biridir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Verileri daha k\u00fc\u00e7\u00fck alt k\u00fcmelere b\u00f6len ve girdi \u00f6zelliklerine dayal\u0131 bir dizi karar\u0131 takip ederek bir tahminde bulunan a\u011fa\u00e7 tabanl\u0131 bir modeldir. A\u011fa\u00e7taki her bir d\u00fc\u011f\u00fcm, \u00f6zelliklerden biri \u00fczerindeki bir testi temsil eder ve dallar bu testin sonucunu temsil eder.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Dallar\u0131n sonu bir tahmin veya bir s\u0131n\u0131f etiketi ile temsil edilir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Karar a\u011fa\u00e7lar\u0131n\u0131n en g\u00fczel yan\u0131, makine \u00f6\u011frenimi konusunda uzman olmayan ki\u015filer i\u00e7in bile anla\u015f\u0131lmas\u0131 ve yorumlanmas\u0131n\u0131n kolay olmas\u0131d\u0131r. Ayr\u0131ca hem kategorik hem de say\u0131sal verileri i\u015fleyebilirler, bu da onu daha pop\u00fcler ve \u00e7ok y\u00f6nl\u00fc hale getirir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">En \u00fcnl\u00fc yakla\u015f\u0131m, karar a\u011fa\u00e7lar\u0131n\u0131 rastgele orman veya gradyan art\u0131rma gibi topluluk algoritmalar\u0131nda kullanmakt\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Karar a\u011fa\u00e7lar\u0131n\u0131n ayk\u0131r\u0131 de\u011ferlere ve eksik de\u011ferlere kar\u015f\u0131 sa\u011flaml\u0131\u011f\u0131n\u0131 da g\u00f6z ard\u0131 etmemeliyiz; bu, ayk\u0131r\u0131 de\u011ferlerden etkilenebilecek verilere d\u00fczg\u00fcn bir e\u011fri uydurmaya \u00e7al\u0131\u015fmak yerine her d\u00fc\u011f\u00fcmde ikili b\u00f6lmelerden kaynaklanmaktad\u0131r.<\/span><\/p>\n<h3 id=\"k-en-yakin-komsular\"><b>K-en yak\u0131n kom\u015fular<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">K-en yak\u0131n kom\u015fu (KNN), hem s\u0131n\u0131fland\u0131rma hem de regresyon problemlerini \u00e7\u00f6zmek i\u00e7in kullan\u0131lan yayg\u0131n olarak kullan\u0131lan basit ama g\u00fc\u00e7l\u00fc bir makine \u00f6\u011frenimi algoritmas\u0131d\u0131r. KNN, veri noktas\u0131n\u0131 en yak\u0131n K kom\u015fu noktas\u0131na g\u00f6re s\u0131n\u0131fland\u0131rmakla ilgilidir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Algoritma, mevcut t\u00fcm verileri depolayarak ve ard\u0131ndan yeni bir veri noktas\u0131 i\u00e7in bu depodaki mesafe a\u00e7\u0131s\u0131ndan kendisine en yak\u0131n K veri noktas\u0131n\u0131 bularak \u00e7al\u0131\u015f\u0131r. Tahmin daha sonra, \u00e7\u00f6zmeniz gereken soruna ba\u011fl\u0131 olarak, K en yak\u0131n kom\u015funun \u00e7o\u011funluk s\u0131n\u0131f\u0131na veya de\u011ferlerinin ortalamas\u0131na dayan\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">KNN&#8217;nin avantajlar\u0131 kolay uygulanmas\u0131 ve nispeten d\u00fc\u015f\u00fck hesaplama maliyetleridir, bu da onu g\u00f6r\u00fcnt\u00fc ve konu\u015fma tan\u0131ma, t\u0131bbi te\u015fhis, finans ve di\u011fer bir\u00e7ok alandaki uygulamalar i\u00e7in kullan\u0131\u015fl\u0131 bir se\u00e7im haline getirir. Bununla birlikte, K se\u00e7iminden ve uzakl\u0131k metri\u011fi t\u00fcr\u00fcnden etkilenebilece\u011finden do\u011frulu\u011fu konusunda dikkatli olman\u0131z gerekir.<\/span><\/p>\n<h2 id=\"denetimli-ogrenme-ve-alternatifleri\"><b>Denetimli \u00d6\u011frenme ve Alternatifleri<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Modeller sadece etiketli verilere dayanarak \u00f6\u011frenmez. Bu noktada denetimsiz makine \u00f6\u011frenimi devreye girer.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Denetimli bir \u00f6\u011frenme modeli etiketli girdi ve \u00e7\u0131kt\u0131 verilerini kullan\u0131yorsa, denetimsiz bir \u00f6\u011frenme algoritmas\u0131 etiketsiz verilerin yap\u0131s\u0131n\u0131 ke\u015ffetmek i\u00e7in kendi ba\u015f\u0131na \u00e7al\u0131\u015f\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Denetimsiz \u00f6\u011frenme, uzmanlar\u0131n verilerde ne arayaca\u011f\u0131 konusunda hi\u00e7bir fikri olmad\u0131\u011f\u0131nda i\u015fe yarar. Denetimli \u00f6\u011frenmenin aksine, a\u00e7\u0131klay\u0131c\u0131 modelleme ve \u00f6r\u00fcnt\u00fc alg\u0131lama gibi daha karma\u015f\u0131k g\u00f6revler i\u00e7in en uygun y\u00f6ntemdir.<\/span><\/p>\n<h3 id=\"denetimsiz-ogrenme\"><b>Denetimsiz \u00d6\u011frenme<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Denetimsiz \u00f6\u011frenme hakk\u0131nda bilinmesi gerekenler a\u015fa\u011f\u0131daki \u015fekildedir:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimsiz \u00f6\u011frenme, bir veri k\u00fcmesindeki bilinmeyen \u00f6r\u00fcnt\u00fcleri bulmak i\u00e7in \u00f6zellikle yararl\u0131d\u0131r.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Kategorizasyon i\u00e7in gereken \u00f6zelliklerin bulunmas\u0131na yard\u0131mc\u0131 olur.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Resimlerinizin, videolar\u0131n\u0131z\u0131n veya sa\u011flanan herhangi bir verinin a\u00e7\u0131klamal\u0131 veya etiketli olmas\u0131 gerekmez.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimsiz \u00f6\u011frenme, ham girdi verilerini nas\u0131l analiz etti\u011fine tan\u0131k olabilecekleri i\u00e7in \u00f6zellikle veri bilimi alan\u0131nda yeni ba\u015flayanlar i\u00e7in yararl\u0131d\u0131r.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Yukar\u0131da bahsedilenlerle birlikte, denetimli ve denetimsiz \u00f6\u011frenme modelleri aras\u0131ndaki temel farklardan birinin algoritmalar\u0131n\u0131n e\u011fitilme \u015fekli oldu\u011fu s\u00f6ylenebilir. Denetimli \u00f6\u011frenme modellerinin verileri ke\u015ffetme ve elde etme \u015fekli olduk\u00e7a basittir, \u00e7\u00fcnk\u00fc bunu yapma \u00f6zg\u00fcrl\u00fc\u011f\u00fcne sahiptirler. Denetimsiz \u00f6\u011frenme algoritmalar\u0131 ise e\u011fitim seti olarak etiketlenmemi\u015f verilerle u\u011fra\u015f\u0131rlar.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Denetimsiz makine \u00f6\u011freniminde \u00e7\u0131kt\u0131 bilinmedi\u011finden, e\u011fitim daha karma\u015f\u0131k hale gelir, ayr\u0131ca \u00e7ok say\u0131da s\u0131n\u0131fland\u0131r\u0131lmam\u0131\u015f veri k\u00fcmesiyle \u00e7al\u0131\u015fmas\u0131 ve bunlardaki yeni \u00f6r\u00fcnt\u00fcleri tan\u0131mas\u0131 gerekir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Burada, denetimsiz \u00f6\u011frenmenin iki ana b\u00f6l\u00fcm\u00fcn\u00fc k\u0131saca a\u00e7\u0131klayabiliriz: k\u00fcmeleme ve ili\u015fkilendirme.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">K\u00fcmeleme, kategorize edilmemi\u015f bir veri koleksiyonunda bir \u00f6r\u00fcnt\u00fc bulmay\u0131 gerektirir. K\u00fcmeleme algoritmalar\u0131 verileri i\u015fler ve verilerde var olan do\u011fal k\u00fcmeleri bulur. Bilgisayarla g\u00f6rme m\u00fchendisleri, algoritman\u0131n ka\u00e7 k\u00fcme tan\u0131mlamas\u0131 gerekti\u011fini de de\u011fi\u015ftirebilir. Bu k\u00fcmelerle ilgili her t\u00fcrl\u00fc ayr\u0131nt\u0131 buna g\u00f6re ayarlanabilir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u0130li\u015fkilendirme tekni\u011fi, b\u00fcy\u00fck veri tabanlar\u0131ndaki de\u011fi\u015fkenler aras\u0131nda var olan ili\u015fkileri bulmakla ilgilidir. Uzmanlar veri nesneleri aras\u0131nda kolayca ili\u015fki kurabilir. \u00d6rne\u011fin, yeni bir ev sat\u0131n alan bireylerin yeni mobilya sat\u0131n alma olas\u0131l\u0131\u011f\u0131 y\u00fcksektir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">K-ortalamalar k\u00fcmeleme ve birliktelik kurallar\u0131 yayg\u0131n denetimsiz \u00f6\u011frenme algoritmas\u0131 \u00f6rnekleridir.<\/span><\/p>\n<h3 id=\"yari-denetimli-ogrenme\"><b>Yar\u0131 Denetimli \u00d6\u011frenme<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">\u00d6nceki iki makine \u00f6\u011frenimi t\u00fcr\u00fcnde, e\u011fitime yard\u0131mc\u0131 olmak i\u00e7in ya etiketli ya da etiketsiz veriler vard\u0131r. Yar\u0131 denetimli makine \u00f6\u011frenimi bu iki teknik aras\u0131nda yer al\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Veri etiketleme, y\u00fcksek e\u011fitimli insan kaynaklar\u0131 gerektiren pahal\u0131 ve zaman al\u0131c\u0131 bir s\u00fcre\u00e7tir. Bu ba\u011flamda, etiketlerin \u00e7o\u011fu g\u00f6zlemde mevcut olmad\u0131\u011f\u0131, ancak sadece bir avu\u00e7ta mevcut oldu\u011fu durumlar vard\u0131r ve bu, yar\u0131 denetimli makine \u00f6\u011freniminin devreye girdi\u011fi yerdir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Yar\u0131 denetimli makine \u00f6\u011frenimi, girdi de\u011fi\u015fkenlerinin yap\u0131s\u0131n\u0131 ke\u015ffederek ve \u00f6\u011frenerek denetimli ve denetimsiz \u00f6\u011frenme aras\u0131nda kalan sorunlar\u0131 \u00e7\u00f6zmeye \u00e7al\u0131\u015f\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hem etiketli hem de etiketsiz g\u00f6r\u00fcnt\u00fcler i\u00e7eren bir foto\u011fraf ar\u015fivi \u00f6rne\u011fini ele alal\u0131m. Verilerin bir k\u0131sm\u0131 zaten etiketlenmi\u015ftir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Yar\u0131 denetimli \u00f6\u011frenme kavram\u0131 olduk\u00e7a basittir; kullan\u0131c\u0131 t\u00fcm veri k\u00fcmesine etiket sa\u011flamak yerine verilerin k\u00fc\u00e7\u00fck bir b\u00f6l\u00fcm\u00fcn\u00fc manuel olarak etiketler.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Daha sonra, ayn\u0131 etiketli veriler bir veri modeli olarak kullan\u0131l\u0131r ve bu model daha sonra b\u00fcy\u00fck miktarda etiketsiz veriye uygulan\u0131r. Yar\u0131 denetimli \u00f6\u011frenme, az miktarda etiketli veri ve b\u00fcy\u00fck miktarda etiketsiz veri ile \u00e7al\u0131\u015f\u0131r, bu da manuel a\u00e7\u0131klama maliyetini en aza indirir ve veri haz\u0131rlama s\u00fcresini k\u0131salt\u0131r.<\/span><\/p>\n<h3 id=\"pekistirmeli-ogrenme\"><b>Peki\u015ftirmeli \u00d6\u011frenme<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Peki\u015ftirmeli \u00f6\u011frenme, \u00f6d\u00fcl\u00fc en \u00fcst d\u00fczeye \u00e7\u0131karacak ve riski en aza indirecek \u015fekilde hareket etmek i\u00e7in \u00e7evre ile etkile\u015fimden elde edilen g\u00f6zlemi kullan\u0131r. Bir algoritma olarak, t\u00fcm olas\u0131l\u0131klar\u0131 ke\u015ffedene kadar \u00e7evresini s\u00fcrekli olarak inceler.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Peki\u015ftirmeli \u00f6\u011frenme, ba\u015fka hi\u00e7bir makine \u00f6\u011frenme algoritmas\u0131n\u0131n yapamayaca\u011f\u0131 \u00e7e\u015fitli karma\u015f\u0131k sorunlar\u0131 \u00e7\u00f6zme yetene\u011fine sahiptir. Makinelerin maksimum performans elde etmek i\u00e7in belirli bir ba\u011flamda ideal davran\u0131\u015f\u0131 otomatik olarak belirlemesine olanak tan\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bu kategorideki yayg\u0131n algoritmalar aras\u0131nda q-learning, temporal difference ve deep adversarial networks yer almaktad\u0131r. Bu algoritmalar otonom ara\u00e7lar, robotik eller ve bilgisayarla oynanan masa oyunlar\u0131 gibi alanlar\u0131 kapsamaktad\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Peki\u015ftirmeli \u00f6\u011frenmenin faydalar\u0131ndan baz\u0131lar\u0131, bir sorunu birka\u00e7 k\u00fc\u00e7\u00fck \u00f6l\u00e7ekli soruna b\u00f6lmek yerine bir b\u00fct\u00fcn olarak odaklanmay\u0131, do\u011frudan arac\u0131larla ve \u00e7evresiyle olan etkile\u015fimlerinden veri elde etmeyi ve farkl\u0131 ortamlarda uyum sa\u011flama ve \u00e7al\u0131\u015fma yetene\u011fini i\u00e7erir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Peki\u015ftirmeli \u00f6\u011frenme, en s\u0131cak ara\u015ft\u0131rma konular\u0131ndan biri olmaya devam ediyor ve hen\u00fcz yayg\u0131n bir \u015fekilde benimsenme yolunda ilerliyor.<\/span><\/p>\n<h2 id=\"denetimli-makine-ogrenimi-algoritmalarinin-avantajlari\"><b>Denetimli Makine \u00d6\u011frenimi Algoritmalar\u0131n\u0131n Avantajlar\u0131<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme algoritmalar\u0131n\u0131n avantajlar\u0131 a\u015fa\u011f\u0131daki \u015fekildedir:\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Tahmini do\u011fruluk:<\/b><span style=\"font-weight: 400;\"> Denetimli model b\u00fcy\u00fck ve \u00e7e\u015fitli etiketli veri k\u00fcmeleri \u00fczerinde e\u011fitilirse, etkileyici y\u00fcksek tahmini do\u011fruluk elde edebilir. Hedefiniz son derece do\u011fru modellere sahip olmaksa ve elinizde uygun veri k\u00fcmesi varsa, denetimli \u00f6\u011frenme modelleri genellikle iyi bir se\u00e7imdir.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Net hedefler:<\/b><span style=\"font-weight: 400;\"> Denetimli \u00f6\u011frenme durumunda, e\u011fitim verilerinin s\u0131n\u0131flar\u0131 ve de\u011ferleri bilinir ve girdileri \u00e7\u0131kt\u0131larla e\u015fle\u015ftirmenin net bir amac\u0131 vard\u0131r. Algoritman\u0131n bu hedefe g\u00f6re ne kadar iyi performans g\u00f6sterdi\u011fini analiz ederek, belirli bir g\u00f6rev i\u00e7in optimize etmek daha kolay hale gelir ve daha verimli bir problem \u00e7\u00f6zme deneyimi sa\u011flar.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Geni\u015f uygulama yelpazesi:<\/b><span style=\"font-weight: 400;\"> Denetimli \u00f6\u011frenme \u00e7ok y\u00f6nl\u00fcd\u00fcr ve s\u0131n\u0131fland\u0131rma, regresyon ve yap\u0131land\u0131r\u0131lm\u0131\u015f tahmin problemlerine uygulanmas\u0131na izin vererek \u00e7e\u015fitli g\u00f6revler i\u00e7in esnek bir y\u00f6ntem haline getirir\u200d.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Uygulamas\u0131 daha kolayd\u0131r:<\/b><span style=\"font-weight: 400;\"> Denetimli \u00f6\u011frenme modellerinin uygulanmas\u0131 ve anla\u015f\u0131lmas\u0131 denetimsiz algoritmalara k\u0131yasla genellikle daha kolayd\u0131r, bu da onu bir\u00e7ok uygulay\u0131c\u0131 i\u00e7in daha eri\u015filebilir bir se\u00e7enek haline getirir. Ayr\u0131ca, geni\u015f bir algoritma havuzu mevcuttur.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Denetimsiz \u00f6\u011frenmenin verilerdeki gizli \u00f6r\u00fcnt\u00fcleri ortaya \u00e7\u0131karma yetene\u011fi gibi kendi avantajlar\u0131 olsa da, denetimli \u00f6\u011frenme \u00e7o\u011fu ger\u00e7ek d\u00fcnya problemini \u00e7\u00f6zmek i\u00e7in hala \u00e7ok daha yayg\u0131nd\u0131r.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bulutistan hizmetlerinin detaylar\u0131na ula\u015fmak i\u00e7in <\/span><a href=\"https:\/\/bulutistan.com\/cloud\/\"><span style=\"font-weight: 400;\">t\u0131klay\u0131n\u0131z<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"denetimli-ogrenmenin-dezavantajlari\"><b>Denetimli \u00d6\u011frenmenin Dezavantajlar\u0131<\/b><\/h2>\n<p><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme ile ilgili temel sorun etiketli veriye duyulan ihtiya\u00e7t\u0131r. Denetimli bir \u00f6\u011frenme algoritmas\u0131n\u0131 e\u011fitmek i\u00e7in, hem girdileri hem de ilgili \u00e7\u0131kt\u0131lar\u0131 i\u00e7eren b\u00fcy\u00fck ve \u00e7e\u015fitli etiketli bir veri k\u00fcmesine ihtiyac\u0131n\u0131z vard\u0131r. \u00d6zellikle karma\u015f\u0131k g\u00f6revler i\u00e7in bunu elde etmek zor ve zaman al\u0131c\u0131 olabilir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Bazen manuel ek a\u00e7\u0131klamalar olmadan veri bulabilirsiniz. \u00d6rne\u011fin, arama motorlar\u0131, \u00f6neri sistemleri, hisse senedi fiyatlar\u0131 veya banka temerr\u00fctleri. Bu veriler zaten etiketlenmi\u015ftir.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Ancak \u00e7o\u011fu durumda bu t\u00fcr etiketli verileri ger\u00e7ek d\u00fcnyada bulmak \u00e7ok zor, hatta imkans\u0131zd\u0131r. Bu y\u00fczden veriler manuel olarak etiketlenmelidir. Denetimli \u00f6\u011frenme tekniklerinin t\u00fcm dezavantajlar\u0131 bu ger\u00e7ekten kaynaklanmaktad\u0131r.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme modellerinin performans\u0131 b\u00fcy\u00fck \u00f6l\u00e7\u00fcde sa\u011flanan e\u011fitim verilerinin kalitesine ba\u011fl\u0131d\u0131r.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimli makine \u00f6\u011freniminde b\u00fcy\u00fck verileri etiketlemek zor ve zaman al\u0131c\u0131d\u0131r.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Test verilerinin da\u011f\u0131l\u0131m\u0131 e\u011fitim veri setinden \u00f6nemli \u00f6l\u00e7\u00fcde farkl\u0131 ise, denetimli makine \u00f6\u011freniminde do\u011fru \u00e7\u0131kt\u0131y\u0131 tahmin etmek son derece zordur.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimli makine \u00f6\u011frenimi verileri kendi ba\u015f\u0131na s\u0131n\u0131fland\u0131ramaz.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Karma\u015f\u0131k metinleri tamamlayamamas\u0131, en b\u00fcy\u00fck denetimli \u00f6\u011frenme sorunlar\u0131ndan biri olarak kabul edilir.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denetimli \u00f6\u011frenme t\u00fcm bilgisini insan girdisinden elde etti\u011finden, insan hatas\u0131 olas\u0131l\u0131\u011f\u0131 y\u00fcksek olabilir.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Manuel olarak a\u00e7\u0131klanan veriler \u00fczerinde e\u011fitilen modeller, e\u011fitim veri setinde \u00e7e\u015fitlilik eksikli\u011finden muzdarip olabilir ve bu da verilerin ger\u00e7ek da\u011f\u0131l\u0131m\u0131n\u0131 yans\u0131tmayan \u00f6nyarg\u0131l\u0131 modellere yol a\u00e7abilir. Bu durum, yeterince temsil edilmeyen veya az\u0131nl\u0131k gruplar\u0131nda d\u00fc\u015f\u00fck performansa neden olabilir.<\/span><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"Denetimli \u00f6\u011frenme, modellerin do\u011fru tahminler yapmas\u0131na yard\u0131mc\u0131 olmak i\u00e7in etiketli e\u011fitim verilerini kullanan makine \u00f6\u011freniminin en yayg\u0131n uygulanan&hellip;\n","protected":false},"author":1,"featured_media":4067,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_singular_sidebar":"","csco_page_header_type":"","csco_appearance_grid":"","csco_page_load_nextpost":"","csco_post_video_location":[],"csco_post_video_location_hash":"","csco_post_video_url":"","csco_post_video_bg_start_time":0,"csco_post_video_bg_end_time":0},"categories":[3,17],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.9 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Denetimli ve G\u00f6zetimli \u00d6\u011frenme Nedir? Supervised Learning Genel Bak\u0131\u015f - Bulutistan Blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Denetimli ve G\u00f6zetimli \u00d6\u011frenme Nedir? Supervised Learning Genel Bak\u0131\u015f - Bulutistan Blog\" \/>\n<meta property=\"og:description\" content=\"Denetimli \u00f6\u011frenme, modellerin do\u011fru tahminler yapmas\u0131na yard\u0131mc\u0131 olmak i\u00e7in etiketli e\u011fitim verilerini kullanan makine \u00f6\u011freniminin en yayg\u0131n uygulanan&hellip;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/\" \/>\n<meta property=\"og:site_name\" content=\"Bulutistan Blog\" \/>\n<meta property=\"article:published_time\" content=\"2023-12-14T08:09:29+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-01-20T10:23:42+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/bulutistan.com\/blog\/wp-content\/uploads\/2023\/12\/denetimli-ve-gozetimli-ogrenme-nedir-.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"667\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Bulutistan\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Bulutistan\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"15 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/\",\"url\":\"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/\",\"name\":\"Denetimli ve G\u00f6zetimli \u00d6\u011frenme Nedir? Supervised Learning Genel Bak\u0131\u015f - Bulutistan Blog\",\"isPartOf\":{\"@id\":\"https:\/\/bulutistan.com\/blog\/#website\"},\"datePublished\":\"2023-12-14T08:09:29+00:00\",\"dateModified\":\"2024-01-20T10:23:42+00:00\",\"author\":{\"@id\":\"https:\/\/bulutistan.com\/blog\/#\/schema\/person\/06a4312aff9f5a9fc23e25fe7a27076e\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/bulutistan.com\/blog\/#website\",\"url\":\"https:\/\/bulutistan.com\/blog\/\",\"name\":\"Bulutistan Blog\",\"description\":\"Teknolojide Yol Arkada\u015f\u0131n\u0131z\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/bulutistan.com\/blog\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"tr\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/bulutistan.com\/blog\/#\/schema\/person\/06a4312aff9f5a9fc23e25fe7a27076e\",\"name\":\"Bulutistan\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/bulutistan.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/0b09f693645c754f52af6ce46e1749e1?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/0b09f693645c754f52af6ce46e1749e1?s=96&d=mm&r=g\",\"caption\":\"Bulutistan\"},\"sameAs\":[\"https:\/\/bulutistan.com\/blog\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Denetimli ve G\u00f6zetimli \u00d6\u011frenme Nedir? Supervised Learning Genel Bak\u0131\u015f - Bulutistan Blog","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/","og_locale":"tr_TR","og_type":"article","og_title":"Denetimli ve G\u00f6zetimli \u00d6\u011frenme Nedir? Supervised Learning Genel Bak\u0131\u015f - Bulutistan Blog","og_description":"Denetimli \u00f6\u011frenme, modellerin do\u011fru tahminler yapmas\u0131na yard\u0131mc\u0131 olmak i\u00e7in etiketli e\u011fitim verilerini kullanan makine \u00f6\u011freniminin en yayg\u0131n uygulanan&hellip;","og_url":"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/","og_site_name":"Bulutistan Blog","article_published_time":"2023-12-14T08:09:29+00:00","article_modified_time":"2024-01-20T10:23:42+00:00","og_image":[{"width":1000,"height":667,"url":"https:\/\/bulutistan.com\/blog\/wp-content\/uploads\/2023\/12\/denetimli-ve-gozetimli-ogrenme-nedir-.jpg","type":"image\/jpeg"}],"author":"Bulutistan","twitter_card":"summary_large_image","twitter_misc":{"Yazan:":"Bulutistan","Tahmini okuma s\u00fcresi":"15 dakika"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/","url":"https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/","name":"Denetimli ve G\u00f6zetimli \u00d6\u011frenme Nedir? Supervised Learning Genel Bak\u0131\u015f - Bulutistan Blog","isPartOf":{"@id":"https:\/\/bulutistan.com\/blog\/#website"},"datePublished":"2023-12-14T08:09:29+00:00","dateModified":"2024-01-20T10:23:42+00:00","author":{"@id":"https:\/\/bulutistan.com\/blog\/#\/schema\/person\/06a4312aff9f5a9fc23e25fe7a27076e"},"inLanguage":"tr","potentialAction":[{"@type":"ReadAction","target":["https:\/\/bulutistan.com\/blog\/denetimli-ve-gozetimli-ogrenme-nedir-supervised-learning-genel-bakis\/"]}]},{"@type":"WebSite","@id":"https:\/\/bulutistan.com\/blog\/#website","url":"https:\/\/bulutistan.com\/blog\/","name":"Bulutistan Blog","description":"Teknolojide Yol Arkada\u015f\u0131n\u0131z","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/bulutistan.com\/blog\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"tr"},{"@type":"Person","@id":"https:\/\/bulutistan.com\/blog\/#\/schema\/person\/06a4312aff9f5a9fc23e25fe7a27076e","name":"Bulutistan","image":{"@type":"ImageObject","inLanguage":"tr","@id":"https:\/\/bulutistan.com\/blog\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/0b09f693645c754f52af6ce46e1749e1?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/0b09f693645c754f52af6ce46e1749e1?s=96&d=mm&r=g","caption":"Bulutistan"},"sameAs":["https:\/\/bulutistan.com\/blog"]}]}},"_links":{"self":[{"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/posts\/4068"}],"collection":[{"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/comments?post=4068"}],"version-history":[{"count":4,"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/posts\/4068\/revisions"}],"predecessor-version":[{"id":4170,"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/posts\/4068\/revisions\/4170"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/media\/4067"}],"wp:attachment":[{"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/media?parent=4068"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/categories?post=4068"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/bulutistan.com\/blog\/wp-json\/wp\/v2\/tags?post=4068"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}