{"id":32189,"date":"2025-10-18T20:31:19","date_gmt":"2025-10-18T17:31:19","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/"},"modified":"2025-10-18T20:31:19","modified_gmt":"2025-10-18T17:31:19","slug":"karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/","title":{"rendered":"Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131: Temelden \u0130leri Seviyeye Kapsaml\u0131 Rehber"},"content":{"rendered":"<style>\n    body {\n        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;\n        line-height: 1.6;\n        color: #333;\n        margin: 0 auto;\n        padding: 20px;\n        max-width: 1000px;\n        background-color: #f9f9f9;\n    }\n    h2, h3 {\n        color: #2c3e50;\n        margin-top: 30px;\n        margin-bottom: 15px;\n        border-bottom: 2px solid #3498db;\n        padding-bottom: 5px;\n    }\n    h2 {\n        font-size: 2.2em;\n    }\n    h3 {\n        font-size: 1.8em;\n        border-bottom: 1px dashed #95a5a6;\n    }\n    p {\n        margin-bottom: 15px;\n        text-align: justify;\n    }\n    ul {\n        list-style-type: disc;\n        margin-left: 20px;\n        margin-bottom: 15px;\n    }\n    ol {\n        list-style-type: decimal;\n        margin-left: 20px;\n        margin-bottom: 15px;\n    }\n    li {\n        margin-bottom: 8px;\n    }\n    code {\n        background-color: #ecf0f1;\n        padding: 2px 4px;\n        border-radius: 3px;\n        font-family: 'Consolas', 'Monaco', monospace;\n        color: #c0392b;\n    }\n    pre {\n        background-color: #2d2d2d;\n        color: #f8f8f2;\n        padding: 15px;\n        border-radius: 5px;\n        overflow-x: auto;\n        margin-bottom: 20px;\n    }\n    pre code {\n        background-color: transparent;\n        color: inherit;\n        padding: 0;\n        border-radius: 0;\n    }\n    table {\n        width: 100%;\n        border-collapse: collapse;\n        margin-bottom: 20px;\n        background-color: #fff;\n    }\n    th, td {\n        border: 1px solid #ddd;\n        padding: 10px;\n        text-align: left;\n    }\n    th {\n        background-color: #3498db;\n        color: white;\n    }\n    tr:nth-child(even) {\n        background-color: #f2f2f2;\n    }\n    .uzman-ipucu {\n        background-color: #e8f6f3;\n        border-left: 5px solid #1abc9c;\n        padding: 15px;\n        margin: 20px 0;\n        font-style: italic;\n        color: #21618c;\n    }<\/p>\n<p>    \/* Mobil uyumluluk *\/\n    @media (max-width: 768px) {\n        body {\n            padding: 10px;\n        }\n        h2 {\n            font-size: 1.8em;\n        }\n        h3 {\n            font-size: 1.5em;\n        }\n        table, thead, tbody, th, td, tr {\n            display: block;\n        }\n        thead tr {\n            position: absolute;\n            top: -9999px;\n            left: -9999px;\n        }\n        tr {\n            border: 1px solid #ccc;\n            margin-bottom: 10px;\n        }\n        td {\n            border: none;\n            border-bottom: 1px solid #eee;\n            position: relative;\n            padding-left: 50%;\n            text-align: right;\n        }\n        td:before {\n            position: absolute;\n            top: 6px;\n            left: 6px;\n            width: 45%;\n            padding-right: 10px;\n            white-space: nowrap;\n            text-align: left;\n            font-weight: bold;\n        }\n        \/* Etiketleri manuel olarak eklemeliyiz *\/\n        td:nth-of-type(1):before { content: \"\u00d6zellik:\"; }\n        td:nth-of-type(2):before { content: \"A\u00e7\u0131klama:\"; }\n        td:nth-of-type(3):before { content: \"Kullan\u0131m:\"; }\n    }\n<\/style>\n<p>B\u00fcy\u00fck veri k\u00fcmeleriyle kar\u015f\u0131la\u015ft\u0131\u011f\u0131m\u0131zda, bu y\u0131\u011f\u0131nlar\u0131n i\u00e7erisinde gizlenmi\u015f de\u011ferli bilgileri ve karar alma kal\u0131plar\u0131n\u0131 nas\u0131l ortaya \u00e7\u0131karabiliriz? \u0130\u015fte tam bu noktada, Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131 devreye giriyor. Makine \u00f6\u011freniminin en sezgisel ve g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131ndan biri olan karar a\u011fa\u00e7lar\u0131, karma\u015f\u0131k verileri anla\u015f\u0131l\u0131r bir yap\u0131ya d\u00f6n\u00fc\u015ft\u00fcrerek tahmin yapmam\u0131z\u0131 ve karar alma s\u00fcre\u00e7lerini optimize etmemizi sa\u011flar. Bu kapsaml\u0131 rehberde, karar a\u011fa\u00e7lar\u0131n\u0131n temel prensiplerinden ileri d\u00fczey uygulamalar\u0131na kadar her \u015feyi ke\u015ffedecek, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle bu b\u00fcy\u00fcleyici algoritmay\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz.<\/p>\n<p>Karar a\u011fa\u00e7lar\u0131, hem s\u0131n\u0131fland\u0131rma hem de regresyon problemleri i\u00e7in kullan\u0131lan, g\u00f6zlemleri belirli kurallar dizisine g\u00f6re dallara ay\u0131rarak nihai bir karara veya tahmine ula\u015fan bir makine \u00f6\u011frenimi modelidir. Adeta bir ak\u0131\u015f \u015femas\u0131 gibi \u00e7al\u0131\u015fan bu algoritma, her bir d\u00fc\u011f\u00fcmde bir \u00f6zniteli\u011fin de\u011ferini sorgulayarak ve bu sorgunun sonucuna g\u00f6re veriyi farkl\u0131 dallara y\u00f6nlendirerek \u00e7al\u0131\u015f\u0131r. Bu yap\u0131, algoritman\u0131n i\u015fleyi\u015fini hem \u015feffaf hem de yorumlanabilir k\u0131lar, bu da onu bir\u00e7ok sekt\u00f6rde pop\u00fcler bir se\u00e7im haline getirir.<\/p>\n<p>Bir an i\u00e7in bir doktorun te\u015fhis s\u00fcrecini d\u00fc\u015f\u00fcn\u00fcn: hastan\u0131n semptomlar\u0131n\u0131 (ate\u015f var m\u0131?, \u00f6ks\u00fcr\u00fck var m\u0131?, bo\u011faz a\u011fr\u0131s\u0131 var m\u0131?) tek tek de\u011ferlendirir ve her cevaba g\u00f6re sonraki ad\u0131m\u0131 belirler. Karar a\u011fa\u00e7lar\u0131 da benzer bir mant\u0131kla \u00e7al\u0131\u015f\u0131r; verideki \u00f6zellikleri bir dizi &#8220;e\u011fer-ise&#8221; kural\u0131na d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu kurallar, veriyi giderek daha homojen alt gruplara ay\u0131rarak sonunda belirli bir s\u0131n\u0131fland\u0131rmaya veya say\u0131sal bir tahmine ula\u015f\u0131r. \u00d6rne\u011fin, bir banka kredi ba\u015fvurusunu de\u011ferlendirirken m\u00fc\u015fterinin gelir d\u00fczeyi, kredi ge\u00e7mi\u015fi ve ya\u015f\u0131 gibi \u00f6zelliklerine bakarak kredi verme karar\u0131n\u0131 bir karar a\u011fac\u0131 gibi verebilir. Algoritman\u0131n bu kadar \u00f6nemli olmas\u0131n\u0131n temel nedenlerinden biri, karma\u015f\u0131k karar alma s\u00fcre\u00e7lerini insan g\u00f6z\u00fcyle bile kolayca takip edilebilir bir formatta sunabilmesidir.<\/p>\n<p>Karar a\u011fa\u00e7lar\u0131, basitli\u011fi ve g\u00fc\u00e7l\u00fc tahmin yetene\u011fi aras\u0131ndaki dengeyi ba\u015far\u0131yla kurar. Di\u011fer bir\u00e7ok makine \u00f6\u011frenimi algoritmas\u0131n\u0131n &#8220;kara kutu&#8221; do\u011fas\u0131n\u0131n aksine, karar a\u011fa\u00e7lar\u0131 karar verme s\u00fcrecinin her ad\u0131m\u0131n\u0131 a\u00e7\u0131k\u00e7a g\u00f6sterir. Bu \u015feffafl\u0131k, \u00f6zellikle i\u015f kararlar\u0131 al\u0131n\u0131rken veya denetlenebilirlik gerektiren alanlarda (sa\u011fl\u0131k, finans gibi) b\u00fcy\u00fck bir avantaj sa\u011flar. Ayr\u0131ca, veri \u00f6n i\u015fleme ihtiyac\u0131n\u0131n di\u011fer algoritmalara g\u00f6re daha az olmas\u0131 (\u00f6rne\u011fin, \u00f6zellik \u00f6l\u00e7eklendirme veya normalle\u015ftirme \u00e7o\u011fu zaman gerekmez) ve hem kategorik hem de say\u0131sal verilerle do\u011frudan \u00e7al\u0131\u015fabilmesi, karar a\u011fa\u00e7lar\u0131n\u0131 veri bilimcileri i\u00e7in vazge\u00e7ilmez bir ara\u00e7 haline getirir. Bu y\u00fczden, temel d\u00fczeyde bile makine \u00f6\u011frenimi hakk\u0131nda bilgi edinmek isteyen herkesin karar a\u011fa\u00e7lar\u0131n\u0131 anlamas\u0131 b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r.<\/p>\n<h3>Karar A\u011fa\u00e7lar\u0131n\u0131n Temel Bile\u015fenleri Nelerdir?<\/h3>\n<p>Bir karar a\u011fac\u0131n\u0131n yap\u0131s\u0131n\u0131 anlamak, algoritman\u0131n nas\u0131l \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131 kavraman\u0131n ilk ad\u0131m\u0131d\u0131r. Bir karar a\u011fac\u0131 temelde \u00fc\u00e7 ana bile\u015fenden olu\u015fur ve bunlar hiyerar\u015fik bir d\u00fczen i\u00e7erisinde birbirine ba\u011flan\u0131r:<\/p>\n<ol>\n<li><strong>K\u00f6k D\u00fc\u011f\u00fcm (Root Node):<\/strong> A\u011fac\u0131n en \u00fcst\u00fcnde yer alan ve t\u00fcm veri setini temsil eden ba\u015flang\u0131\u00e7 noktas\u0131d\u0131r. Hen\u00fcz hi\u00e7bir karar al\u0131nmam\u0131\u015f veya hi\u00e7bir \u00f6zellik sorgulanmam\u0131\u015ft\u0131r.<\/li>\n<li><strong>\u0130\u00e7 D\u00fc\u011f\u00fcm (Internal Node) \/ Karar D\u00fc\u011f\u00fcm\u00fc (Decision Node):<\/strong> K\u00f6k d\u00fc\u011f\u00fcmden sonra gelen ve veri setini belirli bir \u00f6zelli\u011fe g\u00f6re iki veya daha fazla alt k\u00fcmeye b\u00f6len d\u00fc\u011f\u00fcmlerdir. Her i\u00e7 d\u00fc\u011f\u00fcm bir &#8220;soru&#8221; sorar (\u00f6rne\u011fin, &#8220;Gelir > 50000 mi?&#8221;). Bu sorunun cevab\u0131na g\u00f6re veri farkl\u0131 dallara ayr\u0131l\u0131r.<\/li>\n<li><strong>Yaprak D\u00fc\u011f\u00fcm (Leaf Node) \/ Terminal D\u00fc\u011f\u00fcm (Terminal Node):<\/strong> A\u011fac\u0131n en alt\u0131nda yer alan ve art\u0131k daha fazla b\u00f6l\u00fcnmeyen d\u00fc\u011f\u00fcmlerdir. Bu d\u00fc\u011f\u00fcmler, bir s\u0131n\u0131fland\u0131rma problemi i\u00e7in nihai karar\u0131 (\u00f6rne\u011fin, &#8220;Evet&#8221; veya &#8220;Hay\u0131r&#8221;) veya bir regresyon problemi i\u00e7in tahmini say\u0131sal de\u011feri (\u00f6rne\u011fin, &#8220;15000 TL&#8221;) temsil eder.<\/li>\n<li><strong>Dal (Branch) \/ Kenar (Edge):<\/strong> D\u00fc\u011f\u00fcmler aras\u0131ndaki ba\u011flant\u0131lar\u0131 ifade eder. Her dal, bir karar\u0131n sonucunu (\u00f6rne\u011fin, &#8220;Evet&#8221; veya &#8220;Hay\u0131r&#8221; cevab\u0131n\u0131) temsil eder ve veriyi bir d\u00fc\u011f\u00fcmden di\u011ferine ta\u015f\u0131r.<\/li>\n<\/ol>\n<p>Bu bile\u015fenler, bir araya gelerek veriyi en iyi \u015fekilde s\u0131n\u0131fland\u0131racak veya tahmin edecek bir dizi kural olu\u015fturur. Karar a\u011fac\u0131 algoritmas\u0131, her ad\u0131mda hangi \u00f6zelli\u011fin ve hangi b\u00f6lme noktas\u0131n\u0131n (threshold) veriyi en saf (homojen) alt k\u00fcmelere ay\u0131raca\u011f\u0131n\u0131 belirlemek i\u00e7in \u00e7e\u015fitli kriterler kullan\u0131r. Bu &#8220;safl\u0131k&#8221; kavram\u0131, genellikle Gini Safs\u0131zl\u0131\u011f\u0131 (Gini Impurity) veya Entropi (Entropy) gibi metriklerle \u00f6l\u00e7\u00fcl\u00fcr. Amac\u0131m\u0131z, her b\u00f6lmeyle birlikte belirsizli\u011fi azaltarak yaprak d\u00fc\u011f\u00fcmlere ula\u015ft\u0131\u011f\u0131m\u0131zda m\u00fcmk\u00fcn oldu\u011funca tek bir s\u0131n\u0131fa ait g\u00f6zlemlerin bulunmas\u0131n\u0131 sa\u011flamakt\u0131r. Bu yap\u0131 sayesinde, bir veri noktas\u0131n\u0131n hangi yaprak d\u00fc\u011f\u00fcme d\u00fc\u015fece\u011fini takip ederek, o noktan\u0131n tahminini veya s\u0131n\u0131fland\u0131rmas\u0131n\u0131 kolayca anlayabiliriz. Bu da karar a\u011fa\u00e7lar\u0131n\u0131n yorumlanabilirlik a\u00e7\u0131s\u0131ndan neden bu kadar de\u011ferli oldu\u011funu a\u00e7\u0131klar.<\/p>\n<h2>Bir Karar A\u011fac\u0131 Nas\u0131l Olu\u015fturulur? Ad\u0131m Ad\u0131m Rehber<\/h2>\n<p>Bir karar a\u011fac\u0131n\u0131 olu\u015fturma s\u00fcreci, temelde veriyi en iyi \u015fekilde b\u00f6lecek kurallar\u0131 bulma i\u015flemidir. Bu s\u00fcre\u00e7, verinin \u00f6zelliklerine bakarak en &#8220;bilgilendirici&#8221; b\u00f6lmeleri yapmay\u0131 hedefler. Algoritma, a\u011fac\u0131 k\u00f6k d\u00fc\u011f\u00fcmden ba\u015flayarak dallara ay\u0131r\u0131r ve her bir d\u00fc\u011f\u00fcmde veriyi en homojen alt gruplara ay\u0131racak en iyi \u00f6zelli\u011fi ve b\u00f6lme noktas\u0131n\u0131 bulmaya \u00e7al\u0131\u015f\u0131r. Bu b\u00f6lme i\u015flemi, verideki belirsizli\u011fi (veya &#8220;safs\u0131zl\u0131\u011f\u0131&#8221;) azaltmay\u0131 ama\u00e7lar.<\/p>\n<p>\u0130lk ad\u0131m, bir \u00f6zellik se\u00e7mek ve bu \u00f6zelli\u011fe g\u00f6re veriyi b\u00f6lmektir. Diyelim ki m\u00fc\u015fteri churn (m\u00fc\u015fteri kayb\u0131) tahmini yap\u0131yoruz. Hangi \u00f6zelli\u011fin (\u00f6rne\u011fin, &#8220;hizmet s\u00fcresi&#8221;, &#8220;ayl\u0131k harcama&#8221;, &#8220;\u015fikayet say\u0131s\u0131&#8221;) m\u00fc\u015fterilerin churn edip etmemesi \u00fczerinde en b\u00fcy\u00fck etkiye sahip oldu\u011funu bulmam\u0131z gerekir. Algoritma, bu karar\u0131 vermek i\u00e7in belirli matematiksel kriterlere (Gini Safs\u0131zl\u0131\u011f\u0131 veya Entropi gibi) ba\u015fvurur. Bu kriterler, bir b\u00f6lmenin ne kadar iyi oldu\u011funu, yani b\u00f6lme sonucunda olu\u015fan alt k\u00fcmelerin ne kadar &#8220;saf&#8221; oldu\u011funu nicel olarak ifade eder. Saf bir k\u00fcme, m\u00fcmk\u00fcn oldu\u011funca tek bir s\u0131n\u0131fa ait \u00f6rneklerden olu\u015fur.<\/p>\n<p>S\u00fcrekli olarak bu b\u00f6lme i\u015flemini tekrarlar\u0131z: her bir alt k\u00fcme \u00fczerinde, yine en iyi b\u00f6lmeyi yapacak \u00f6zelli\u011fi ve e\u015fi\u011fi buluruz. Bu i\u015flem, a\u015fa\u011f\u0131daki durumlardan biri ger\u00e7ekle\u015fene kadar devam eder:<\/p>\n<ol>\n<li>D\u00fc\u011f\u00fcmdeki t\u00fcm \u00f6rnekler ayn\u0131 s\u0131n\u0131fa ait hale gelir (yani, d\u00fc\u011f\u00fcm saf hale gelir).<\/li>\n<li>Maksimum a\u011fa\u00e7 derinli\u011fine ula\u015f\u0131l\u0131r (\u00f6nceden tan\u0131mlanm\u0131\u015f bir parametredir).<\/li>\n<li>D\u00fc\u011f\u00fcmdeki \u00f6rnek say\u0131s\u0131 belirli bir e\u015fi\u011fin alt\u0131na d\u00fc\u015fer (\u00f6rne\u011fin, bir d\u00fc\u011f\u00fcmde en az <code>N<\/code> \u00f6rnek olmal\u0131).<\/li>\n<li>Daha fazla b\u00f6lme, safs\u0131zl\u0131kta \u00f6nemli bir iyile\u015fme sa\u011flamaz.<\/li>\n<\/ol>\n<p>Bu ad\u0131mlar tamamland\u0131\u011f\u0131nda, son d\u00fc\u011f\u00fcmler &#8220;yaprak d\u00fc\u011f\u00fcmler&#8221; haline gelir ve her yaprak d\u00fc\u011f\u00fcm, o d\u00fc\u011f\u00fcme d\u00fc\u015fen \u00f6rneklerin \u00e7o\u011funluk s\u0131n\u0131f\u0131n\u0131 veya ortalama de\u011ferini temsil eder. Bu yap\u0131, bize yeni bir veri noktas\u0131 geldi\u011finde, onu a\u011fa\u00e7tan ge\u00e7irerek hangi yaprak d\u00fc\u011f\u00fcme d\u00fc\u015fece\u011fini belirlememize ve b\u00f6ylece bir tahmin yapmam\u0131za olanak tan\u0131r. A\u011fac\u0131n nas\u0131l b\u00f6l\u00fcnece\u011fini belirleyen kriterleri daha yak\u0131ndan inceleyelim.<\/p>\n<h3>Gini Safs\u0131zl\u0131\u011f\u0131 ve Entropi: Karar Kriterlerini Anlamak<\/h3>\n<p>Karar a\u011fa\u00e7lar\u0131, hangi \u00f6zelli\u011fin veriyi en iyi \u015fekilde b\u00f6lece\u011fine karar verirken &#8220;safl\u0131k&#8221; \u00f6l\u00e7\u00fctlerini kullan\u0131r. En yayg\u0131n kullan\u0131lan iki \u00f6l\u00e7\u00fct Gini Safs\u0131zl\u0131\u011f\u0131 ve Entropi&#8217;dir.<\/p>\n<h4>Gini Safs\u0131zl\u0131\u011f\u0131 (Gini Impurity)<\/h4>\n<p>Gini Safs\u0131zl\u0131\u011f\u0131, bir d\u00fc\u011f\u00fcmdeki \u00f6rneklerin ne kadar kar\u0131\u015ft\u0131r\u0131ld\u0131\u011f\u0131n\u0131, yani ne kadar saf olmad\u0131\u011f\u0131n\u0131 \u00f6l\u00e7er. Matematiksel olarak, bir d\u00fc\u011f\u00fcmdeki rastgele se\u00e7ilen iki \u00f6rne\u011fin farkl\u0131 s\u0131n\u0131flara ait olma olas\u0131l\u0131\u011f\u0131d\u0131r. E\u011fer bir d\u00fc\u011f\u00fcmdeki t\u00fcm \u00f6rnekler ayn\u0131 s\u0131n\u0131fa aitse, Gini safs\u0131zl\u0131\u011f\u0131 0&#8217;d\u0131r (maksimum safl\u0131k). E\u011fer \u00f6rnekler e\u015fit olarak da\u011f\u0131lm\u0131\u015fsa, Gini safs\u0131zl\u0131\u011f\u0131 maksimum de\u011ferine ula\u015f\u0131r (maksimum safs\u0131zl\u0131k).<\/p>\n<p>Form\u00fcl\u00fc \u015fu \u015fekildedir:<br \/>\n    <code>Gini = 1 - \u2211 (p_i)^2<\/code><br \/>\n    Burada <code>p_i<\/code>, d\u00fc\u011f\u00fcmdeki \u00f6rneklerin <code>i<\/code> s\u0131n\u0131f\u0131na ait olma olas\u0131l\u0131\u011f\u0131d\u0131r.<\/p>\n<p>Algoritma, her bir \u00f6zellik i\u00e7in olas\u0131 t\u00fcm b\u00f6lmeleri de\u011ferlendirir ve Gini safs\u0131zl\u0131\u011f\u0131n\u0131 en \u00e7ok azaltan (veya bilgi kazanc\u0131n\u0131 en \u00e7ok art\u0131ran) b\u00f6lmeyi se\u00e7er. \u00d6rne\u011fin, bir b\u00f6lme sonucunda sol ve sa\u011f alt d\u00fc\u011f\u00fcmlerin Gini safs\u0131zl\u0131\u011f\u0131 hesaplan\u0131r ve bu de\u011ferler a\u011f\u0131rl\u0131kl\u0131 ortalamas\u0131 al\u0131narak ana d\u00fc\u011f\u00fcm\u00fcn Gini safs\u0131zl\u0131\u011f\u0131 ile kar\u015f\u0131la\u015ft\u0131r\u0131l\u0131r.<\/p>\n<h4>Entropi (Entropy) ve Bilgi Kazanc\u0131 (Information Gain)<\/h4>\n<p>Entropi, bir d\u00fc\u011f\u00fcmdeki bilginin belirsizli\u011fini veya d\u00fczensizli\u011fini \u00f6l\u00e7en bir kavramd\u0131r. Termodinamikteki entropi kavram\u0131na benzer \u015fekilde, bir d\u00fc\u011f\u00fcmdeki farkl\u0131 s\u0131n\u0131flar\u0131n kar\u0131\u015f\u0131m\u0131 artt\u0131k\u00e7a entropi de artar. Bir d\u00fc\u011f\u00fcmdeki t\u00fcm \u00f6rnekler ayn\u0131 s\u0131n\u0131fa aitse, entropi 0&#8217;d\u0131r (maksimum d\u00fczen). \u00d6rnekler e\u015fit olarak da\u011f\u0131lm\u0131\u015fsa, entropi maksimum de\u011ferine ula\u015f\u0131r (maksimum d\u00fczensizlik).<\/p>\n<p>Form\u00fcl\u00fc \u015fu \u015fekildedir:<br \/>\n    <code>Entropy = -\u2211 p_i * log2(p_i)<\/code><br \/>\n    Yine burada <code>p_i<\/code>, d\u00fc\u011f\u00fcmdeki \u00f6rneklerin <code>i<\/code> s\u0131n\u0131f\u0131na ait olma olas\u0131l\u0131\u011f\u0131d\u0131r.<\/p>\n<p>Bilgi Kazanc\u0131 ise, bir d\u00fc\u011f\u00fcm\u00fcn belirli bir \u00f6zellik kullan\u0131larak b\u00f6l\u00fcnmesi sonucunda entropideki azalmay\u0131 \u00f6l\u00e7er. Algoritma, bilgi kazanc\u0131n\u0131 en \u00fcst d\u00fczeye \u00e7\u0131karan \u00f6zelli\u011fi ve b\u00f6lme noktas\u0131n\u0131 se\u00e7erek en iyi karar\u0131 verir. Yani, bir b\u00f6lmenin ne kadar de\u011ferli oldu\u011funu, veriyi ne kadar &#8220;temizledi\u011fini&#8221; g\u00f6sterir.<\/p>\n<p><code>Bilgi Kazanc\u0131 = Ana D\u00fc\u011f\u00fcm Entropisi - (Sol Alt D\u00fc\u011f\u00fcm Entropisi * Sol D\u00fc\u011f\u00fcm Oran\u0131 + Sa\u011f Alt D\u00fc\u011f\u00fcm Entropisi * Sa\u011f D\u00fc\u011f\u00fcm Oran\u0131)<\/code><\/p>\n<p>Hem Gini hem de Entropi, benzer sonu\u00e7lar \u00fcretme e\u011filimindedir ve genellikle hangisinin kullan\u0131laca\u011f\u0131, hesaplama maliyeti veya belirli bir veri setindeki performans fark\u0131 gibi fakt\u00f6rlere ba\u011fl\u0131d\u0131r. Gini Safs\u0131zl\u0131\u011f\u0131 genellikle daha h\u0131zl\u0131d\u0131r \u00e7\u00fcnk\u00fc logaritmik hesaplamalar i\u00e7ermez. Ancak her ikisi de karar a\u011fac\u0131n\u0131n etkili bir \u015fekilde \u00f6\u011frenmesini ve veriyi anlaml\u0131 alt gruplara ay\u0131rmas\u0131n\u0131 sa\u011flar. Bu kriterler, Karar A\u011fa\u00e7lar\u0131&#8217;n\u0131n &#8220;zekas\u0131n\u0131n&#8221; temelini olu\u015fturur, \u00e7\u00fcnk\u00fc a\u011fac\u0131n her ad\u0131mda nas\u0131l bir b\u00f6lme yapaca\u011f\u0131na matematiksel bir temel sa\u011flar.<\/p>\n<h2>Karar A\u011fa\u00e7lar\u0131 ile Ger\u00e7ek D\u00fcnya Problemlerini \u00c7\u00f6zmek: Vaka Analizi<\/h2>\n<p>Karar a\u011fa\u00e7lar\u0131, basit ama g\u00fc\u00e7l\u00fc yap\u0131s\u0131 sayesinde bir\u00e7ok farkl\u0131 ger\u00e7ek d\u00fcnya probleminde etkili \u00e7\u00f6z\u00fcmler sunar. Gelin, finans sekt\u00f6r\u00fcnden bir \u00f6rnek \u00fczerinden karar a\u011fa\u00e7lar\u0131n\u0131n pratik uygulamas\u0131n\u0131 inceleyelim: <strong>Kredi Risk De\u011ferlendirmesi<\/strong>. Bir bankan\u0131n kredi ba\u015fvurusunda bulunan m\u00fc\u015fterilerin kredi notunu ve \u00f6deme ge\u00e7mi\u015fini analiz ederek bir kredi ba\u015fvurusu i\u00e7in onay verip vermeme karar\u0131n\u0131 otomatik bir \u015fekilde nas\u0131l alabilece\u011fini d\u00fc\u015f\u00fcnelim.<\/p>\n<p>Bankan\u0131n elinde ge\u00e7mi\u015f d\u00f6nemlere ait binlerce m\u00fc\u015fteri verisi bulunmaktad\u0131r. Bu verilerde m\u00fc\u015fterinin ya\u015f\u0131, gelir d\u00fczeyi, \u00e7al\u0131\u015fma s\u00fcresi, mevcut bor\u00e7lar\u0131, kredi kart\u0131 \u00f6deme ge\u00e7mi\u015fi, ev sahibi olup olmad\u0131\u011f\u0131 gibi \u00f6zellikler ve bu m\u00fc\u015fterilerin krediyi geri \u00f6deyip \u00f6demedi\u011fi (yani &#8220;riskli&#8221; mi &#8220;risksiz&#8221; mi oldu\u011fu) bilgisi yer almaktad\u0131r. Bankan\u0131n amac\u0131, yeni bir ba\u015fvuru geldi\u011finde, bu ge\u00e7mi\u015f verilerden \u00f6\u011frendiklerini kullanarak ba\u015fvurunun riskli olup olmad\u0131\u011f\u0131n\u0131 tahmin etmektir.<\/p>\n<p>Karar a\u011fac\u0131 algoritmas\u0131 bu verilerle e\u011fitildi\u011finde, a\u015fa\u011f\u0131daki gibi bir karar s\u00fcreci olu\u015fturabilir:<\/p>\n<ul>\n<li><strong>K\u00f6k D\u00fc\u011f\u00fcm:<\/strong> T\u00fcm ba\u015fvurular.<\/li>\n<li><strong>1. Karar:<\/strong> Ba\u015fvuruyu yapan ki\u015finin &#8220;Kredi Notu Y\u00fcksek&#8221; mi?\n<ul>\n<li><strong>Evet ise:<\/strong> Bu dalda ilerle.<\/li>\n<li><strong>Hay\u0131r ise:<\/strong> Di\u011fer dala ge\u00e7. Belki &#8220;Gelir D\u00fczeyi Y\u00fcksek&#8221; mi diye sorgula.<\/li>\n<\/ul>\n<\/li>\n<li><strong>2. Karar (Kredi Notu Y\u00fcksek olan dalda):<\/strong> Bu ki\u015finin &#8220;Mevcut Bor\u00e7lar\u0131 D\u00fc\u015f\u00fck&#8221; m\u00fc?\n<ul>\n<li><strong>Evet ise:<\/strong> Bu ki\u015filer genellikle risksizdir. (Yaprak D\u00fc\u011f\u00fcm: Krediyi Onayla)<\/li>\n<li><strong>Hay\u0131r ise:<\/strong> Belki &#8220;\u0130\u015f S\u00fcresi Uzun&#8221; mu diye sorgula.<\/li>\n<\/ul>\n<\/li>\n<li><strong>3. Karar (Kredi Notu D\u00fc\u015f\u00fck ve Gelir D\u00fczeyi Y\u00fcksek olan dalda):<\/strong> &#8220;Ev Sahibi&#8221; mi?\n<ul>\n<li><strong>Evet ise:<\/strong> K\u0131smen risksiz olabilir. (Yaprak D\u00fc\u011f\u00fcm: Krediyi \u015eartl\u0131 Onayla \/ Daha D\u00fc\u015f\u00fck Limit Ver)<\/li>\n<li><strong>Hay\u0131r ise:<\/strong> B\u00fcy\u00fck ihtimalle risklidir. (Yaprak D\u00fc\u011f\u00fcm: Krediyi Reddet)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>Bu vaka analizi, karar a\u011fac\u0131n\u0131n karma\u015f\u0131k bir problemi ne kadar yap\u0131land\u0131r\u0131lm\u0131\u015f ve anla\u015f\u0131l\u0131r bir \u015fekilde ele alabilece\u011fini g\u00f6stermektedir. Banka y\u00f6neticileri veya kredi departman\u0131 \u00e7al\u0131\u015fanlar\u0131, Karar A\u011fac\u0131 taraf\u0131ndan verilen kararlar\u0131n nedenlerini kolayca anlayabilir ve a\u00e7\u0131klayabilir. Bu \u015feffafl\u0131k, i\u015f s\u00fcre\u00e7lerinde g\u00fcven olu\u015fturur ve yasal uyumluluk a\u00e7\u0131s\u0131ndan da \u00f6nemlidir. Ayr\u0131ca, a\u011fa\u00e7 \u00fczerinde yap\u0131lan k\u00fc\u00e7\u00fck ayarlamalarla (\u00f6rne\u011fin, b\u00f6lme e\u015fiklerini de\u011fi\u015ftirerek) risk tolerans\u0131 optimize edilebilir. Bu sayede, Karar A\u011fa\u00e7lar\u0131 sadece tahminde bulunmakla kalmaz, ayn\u0131 zamanda karar alma s\u00fcre\u00e7lerini optimize etmek i\u00e7in de g\u00fc\u00e7l\u00fc bir ara\u00e7 haline gelir.<\/p>\n<h3>Python ile Karar A\u011fac\u0131 Uygulamas\u0131: Basit Bir \u00d6rnek<\/h3>\n<p>Python ve <code>scikit-learn<\/code> k\u00fct\u00fcphanesi, karar a\u011fa\u00e7lar\u0131n\u0131 uygulamak i\u00e7in en pop\u00fcler ve kolay yollardan biridir. \u015eimdi, basit bir veri seti \u00fczerinde bir karar a\u011fac\u0131 s\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131 olu\u015ftural\u0131m. Bu \u00f6rnekte, Iris \u00e7i\u00e7e\u011fi veri setini kullanaca\u011f\u0131z. Iris veri seti, \u00fc\u00e7 farkl\u0131 Iris \u00e7i\u00e7e\u011fi t\u00fcr\u00fcn\u00fcn (setosa, versicolor, virginica) sepal ve petal uzunluklar\u0131n\u0131\/geni\u015fliklerini i\u00e7erir.<\/p>\n<p>\u0130lk olarak, gerekli k\u00fct\u00fcphaneleri i\u00e7e aktaral\u0131m ve veri setini y\u00fckleyelim.<\/p>\n<pre><code class=\"language-python\">\nimport pandas as pd\nfrom sklearn.datasets import load_iris\nfrom sklearn.tree import DecisionTreeClassifier, export_graphviz\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nimport graphviz # A\u011fac\u0131 g\u00f6rselle\u015ftirmek i\u00e7in\n\n# Iris veri setini y\u00fckle\niris = load_iris()\nX = iris.data  # \u00d6zellikler (sepal\/petal uzunluk\/geni\u015flik)\ny = iris.target # Hedef de\u011fi\u015fken (\u00e7i\u00e7ek t\u00fcr\u00fc)\n\n# Veriyi e\u011fitim ve test setlerine ay\u0131r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\nprint(\"E\u011fitim seti boyutu (X_train):\", X_train.shape)\nprint(\"Test seti boyutu (X_test):\", X_test.shape)\n<\/pre>\n<p><\/code><\/p>\n<p>Yukar\u0131daki kod blo\u011fu, verimizi haz\u0131rlad\u0131. \u015eimdi bir <code>DecisionTreeClassifier<\/code> modeli olu\u015ftural\u0131m ve onu e\u011fitim verisiyle e\u011fitelim. <code>max_depth<\/code> gibi parametrelerle a\u011fac\u0131n karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 kontrol edebiliriz.<\/p>\n<pre><code class=\"language-python\">\n# Karar A\u011fac\u0131 S\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131n\u0131 olu\u015ftur ve e\u011fit\n# max_depth parametresi a\u015f\u0131r\u0131 uyumu \u00f6nlemek i\u00e7in a\u011fac\u0131n derinli\u011fini s\u0131n\u0131rlar\ndt_classifier = DecisionTreeClassifier(max_depth=3, random_state=42)\ndt_classifier.fit(X_train, y_train)\n\n# Test seti \u00fczerinde tahmin yap\ny_pred = dt_classifier.predict(X_test)\n\n# Modelin do\u011frulu\u011funu de\u011ferlendir\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"Modelin Do\u011fruluk Skoru: {accuracy:.2f}\")\n<\/pre>\n<p><\/code><\/p>\n<p>Modelimizi e\u011fittik ve test seti \u00fczerinde %97 gibi y\u00fcksek bir do\u011fruluk skoru elde ettik. Bu, modelimizin Iris \u00e7i\u00e7e\u011fi t\u00fcrlerini olduk\u00e7a iyi bir \u015fekilde tahmin edebildi\u011fini g\u00f6sterir. Peki, modelimiz hangi kurallar\u0131 \u00f6\u011frendi? Bunu g\u00f6rselle\u015ftirebiliriz:<\/p>\n<pre><code class=\"language-python\">\n# Karar A\u011fac\u0131n\u0131 g\u00f6rselle\u015ftirme (Graphviz gerektirir)\ndot_data = export_graphviz(dt_classifier,\n                           out_file=None,\n                           feature_names=iris.feature_names,\n                           class_names=iris.target_names,\n                           filled=True, rounded=True,\n                           special_characters=True)\ngraph = graphviz.Source(dot_data)\n# graph.render(\"iris_karar_agaci\", format=\"png\", view=True) # A\u011fac\u0131 PNG olarak kaydedip a\u00e7ar\n# graph # Jupyter Notebook veya benzeri ortamlarda a\u011fac\u0131 do\u011frudan g\u00f6sterir\n<\/pre>\n<p><\/code><\/p>\n<div class=\"uzman-ipucu\">\n  Yukar\u0131daki g\u00f6rselle\u015ftirme kodu, karar a\u011fac\u0131n\u0131z\u0131n nas\u0131l karar verdi\u011fini ad\u0131m ad\u0131m g\u00f6rmenizi sa\u011flar. Her bir d\u00fc\u011f\u00fcmdeki ko\u015fullar\u0131, b\u00f6lmeleri ve nihai s\u0131n\u0131fland\u0131rmalar\u0131 inceleyerek modelin i\u015fleyi\u015fini tam olarak anlayabilirsiniz. Bu \u015feffafl\u0131k, karar a\u011fa\u00e7lar\u0131n\u0131n en b\u00fcy\u00fck avantajlar\u0131ndan biridir. <code>max_depth<\/code> de\u011ferini de\u011fi\u015ftirerek a\u011fac\u0131n nas\u0131l farkl\u0131la\u015ft\u0131\u011f\u0131n\u0131 g\u00f6zlemlemek, a\u015f\u0131r\u0131 uyum kavram\u0131n\u0131 anlamak i\u00e7in harika bir yoldur.\n<\/div>\n<p>Bu Python \u00f6rne\u011fi, Karar A\u011fa\u00e7lar\u0131'n\u0131n ne kadar kolay uygulanabilece\u011fini ve ne kadar g\u00fc\u00e7l\u00fc tahminler yapabilece\u011fini g\u00f6stermektedir. Ger\u00e7ek d\u00fcnya senaryolar\u0131nda da benzer ad\u0131mlar\u0131 izleyerek kendi Karar A\u011fac\u0131 modellerinizi olu\u015fturabilir ve karma\u015f\u0131k verilerden anlaml\u0131 i\u00e7g\u00f6r\u00fcler elde edebilirsiniz.<\/p>\n<h2>Karar A\u011fa\u00e7lar\u0131n\u0131n Avantajlar\u0131 ve Dezavantajlar\u0131 Nelerdir?<\/h2>\n<p>Her makine \u00f6\u011frenimi algoritmas\u0131nda oldu\u011fu gibi, Karar A\u011fa\u00e7lar\u0131n\u0131n da kendine \u00f6zg\u00fc avantajlar\u0131 ve dezavantajlar\u0131 bulunmaktad\u0131r. Bu \u00f6zellikleri bilmek, belirli bir problem i\u00e7in do\u011fru algoritmay\u0131 se\u00e7erken bize yol g\u00f6sterir.<\/p>\n<h3>Avantajlar\u0131:<\/h3>\n<ol>\n<li><strong>Yorumlanabilirlik ve \u015eeffafl\u0131k:<\/strong> Belki de en b\u00fcy\u00fck avantaj\u0131 budur. Karar a\u011fa\u00e7lar\u0131, t\u0131pk\u0131 bir ak\u0131\u015f \u015femas\u0131 gibi \u00e7al\u0131\u015ft\u0131\u011f\u0131 i\u00e7in al\u0131nan kararlar\u0131n nedenlerini ve nas\u0131l al\u0131nd\u0131\u011f\u0131n\u0131 kolayca anlamak ve ba\u015fkalar\u0131na a\u00e7\u0131klamak m\u00fcmk\u00fcnd\u00fcr. Bu, \u00f6zellikle \"kara kutu\" modellerin kabul g\u00f6rmedi\u011fi finans, sa\u011fl\u0131k gibi denetlenebilirlik gerektiren sekt\u00f6rlerde kritik \u00f6neme sahiptir.<\/li>\n<li><strong>Veri \u00d6n \u0130\u015fleme \u0130htiyac\u0131n\u0131n Azl\u0131\u011f\u0131:<\/strong> Di\u011fer baz\u0131 algoritmalara k\u0131yasla, Karar A\u011fa\u00e7lar\u0131 veri \u00f6n i\u015fleme ad\u0131mlar\u0131na (\u00f6rne\u011fin, \u00f6zellik \u00f6l\u00e7eklendirme, normalle\u015ftirme) daha az ihtiya\u00e7 duyar. Hem say\u0131sal hem de kategorik verilerle do\u011frudan \u00e7al\u0131\u015fabilirler.<\/li>\n<li><strong>Do\u011frusal Olmayan \u0130li\u015fkileri Yakalayabilme:<\/strong> Veri i\u00e7indeki do\u011frusal olmayan ve karma\u015f\u0131k ili\u015fkileri yakalamada olduk\u00e7a etkilidirler. Bu, \u00f6zellikle veri setindeki de\u011fi\u015fkenler aras\u0131nda karma\u015f\u0131k etkile\u015fimler oldu\u011funda faydal\u0131d\u0131r.<\/li>\n<li><strong>Veri \u00c7e\u015fitlili\u011fi:<\/strong> Hem s\u0131n\u0131fland\u0131rma hem de regresyon problemleri i\u00e7in kullan\u0131labilirler.<\/li>\n<li><strong>Kay\u0131p De\u011ferlere Kar\u015f\u0131 Diren\u00e7:<\/strong> Baz\u0131 Karar A\u011fac\u0131 uygulamalar\u0131, kay\u0131p de\u011ferleri belirli stratejilerle ele alabilir.<\/li>\n<\/ol>\n<h3>Dezavantajlar\u0131:<\/h3>\n<ol>\n<li><strong>A\u015f\u0131r\u0131 Uyum (Overfitting) E\u011filimi:<\/strong> Karar a\u011fa\u00e7lar\u0131, veri setine a\u015f\u0131r\u0131 derecede uyum sa\u011flama e\u011filimindedir. E\u011fer a\u011fa\u00e7 \u00e7ok derin ve karma\u015f\u0131k hale gelirse, e\u011fitim verisindeki g\u00fcr\u00fclt\u00fcy\u00fc veya tesad\u00fcfi desenleri de \u00f6\u011frenerek yeni, g\u00f6r\u00fcnmeyen veriler \u00fczerinde k\u00f6t\u00fc performans g\u00f6sterebilir. Bu durum, budama (pruning) gibi tekniklerle veya maksimum derinlik gibi parametrelerle kontrol edilebilir.<\/li>\n<li><strong>K\u00fc\u00e7\u00fck De\u011fi\u015fikliklere Hassasiyet:<\/strong> E\u011fitim verisindeki k\u00fc\u00e7\u00fck de\u011fi\u015fiklikler bile a\u011fac\u0131n yap\u0131s\u0131nda \u00f6nemli farkl\u0131l\u0131klara yol a\u00e7abilir. Bu, modelin karars\u0131z olmas\u0131na neden olabilir.<\/li>\n<li><strong>Optimum A\u011fac\u0131 Bulman\u0131n Zorlu\u011fu:<\/strong> En iyi Karar A\u011fac\u0131n\u0131 bulmak (k\u00fcresel optimum), hesaplama a\u00e7\u0131s\u0131ndan olduk\u00e7a zordur. \u00c7o\u011fu algoritma, \"a\u00e7g\u00f6zl\u00fc\" (greedy) bir yakla\u015f\u0131mla yerel optimumu bulmaya \u00e7al\u0131\u015f\u0131r, bu da her zaman en iyi k\u00fcresel \u00e7\u00f6z\u00fcm\u00fc garanti etmez.<\/li>\n<li><strong>E\u011fitim Maliyeti:<\/strong> B\u00fcy\u00fck ve y\u00fcksek boyutlu veri setleri i\u00e7in a\u011fa\u00e7 in\u015fa etme s\u00fcreci zaman al\u0131c\u0131 olabilir.<\/li>\n<li><strong>S\u00fcrekli Verilerle \u00c7al\u0131\u015fma Zorlu\u011fu:<\/strong> S\u00fcrekli de\u011fi\u015fkenler i\u00e7in \u00e7ok say\u0131da b\u00f6lme noktas\u0131 potansiyeli oldu\u011fundan, Karar A\u011fa\u00e7lar\u0131 bu t\u00fcr verilerle \u00e7al\u0131\u015f\u0131rken a\u015f\u0131r\u0131 uyum riskini art\u0131rabilir.<\/li>\n<\/ol>\n<p>Bu avantaj ve dezavantajlar g\u00f6z \u00f6n\u00fcnde bulundurularak, Karar A\u011fa\u00e7lar\u0131 genellikle daha g\u00fc\u00e7l\u00fc ve kararl\u0131 modeller olu\u015fturmak i\u00e7in \"topluluk \u00f6\u011frenmesi\" (ensemble learning) y\u00f6ntemlerinin (\u00f6rne\u011fin, Random Forest, Gradient Boosting) bir par\u00e7as\u0131 olarak kullan\u0131l\u0131r. Bu y\u00f6ntemler, birden fazla Karar A\u011fac\u0131n\u0131n g\u00fc\u00e7lerini birle\u015ftirerek tek bir a\u011fac\u0131n zay\u0131fl\u0131klar\u0131n\u0131 gidermeyi hedefler.<\/p>\n<h2>\u0130leri D\u00fczey Konular: A\u015f\u0131r\u0131 Uyum (Overfitting) ve A\u011fa\u00e7 Topluluklar\u0131 (Ensemble Methods)<\/h2>\n<p>Karar a\u011fa\u00e7lar\u0131, basit yap\u0131lar\u0131na ra\u011fmen baz\u0131 ciddi zorluklar bar\u0131nd\u0131r\u0131r. Bunlar\u0131n ba\u015f\u0131nda \"a\u015f\u0131r\u0131 uyum\" gelmektedir. Bu b\u00f6l\u00fcmde, a\u015f\u0131r\u0131 uyum kavram\u0131n\u0131 ve bu sorunu nas\u0131l \u00e7\u00f6zebilece\u011fimizi, ayr\u0131ca karar a\u011fa\u00e7lar\u0131n\u0131n g\u00fcc\u00fcn\u00fc art\u0131rmak i\u00e7in kullan\u0131lan a\u011fa\u00e7 topluluklar\u0131 y\u00f6ntemlerini inceleyece\u011fiz.<\/p>\n<h3>A\u015f\u0131r\u0131 Uyum (Overfitting) Nedir ve Nas\u0131l \u00d6nlenir?<\/h3>\n<p>A\u015f\u0131r\u0131 uyum, bir makine \u00f6\u011frenimi modelinin e\u011fitim verisine o kadar iyi uyum sa\u011flamas\u0131 durumudur ki, e\u011fitim verisindeki rastgele g\u00fcr\u00fclt\u00fcy\u00fc veya gereksiz detaylar\u0131 da \u00f6\u011frenir. Sonu\u00e7 olarak, model e\u011fitim verileri \u00fczerinde m\u00fckemmel performans g\u00f6sterirken, daha \u00f6nce hi\u00e7 g\u00f6rmedi\u011fi yeni veriler \u00fczerinde tahmin yetene\u011fi ciddi \u015fekilde d\u00fc\u015fer. Bir karar a\u011fac\u0131n\u0131n \u00e7ok derin ve karma\u015f\u0131k hale gelmesi, her bir yaprak d\u00fc\u011f\u00fcmde \u00e7ok az \u00f6rnek kalmas\u0131 a\u015f\u0131r\u0131 uyuma yol a\u00e7an yayg\u0131n bir nedendir.<\/p>\n<p>\u00d6rne\u011fin, bir \u00f6\u011frencinin s\u0131navda sadece ezberledi\u011fi sorular\u0131 do\u011fru cevaplay\u0131p, benzer ama farkl\u0131 formattaki sorular\u0131 \u00e7\u00f6zememesi a\u015f\u0131r\u0131 uyuma benzer. Model de e\u011fitim verisindeki \"ezber\" bilgileri \u00f6\u011frenir, ancak genelleme yetene\u011fini kaybeder.<\/p>\n<h3>A\u015f\u0131r\u0131 Uyumu \u00d6nleme Teknikleri Nas\u0131l Kullan\u0131l\u0131r?<\/h3>\n<p>A\u015f\u0131r\u0131 uyumu azaltmak ve modelin genelleme yetene\u011fini art\u0131rmak i\u00e7in \u00e7e\u015fitli teknikler mevcuttur:<\/p>\n<ol>\n<li><strong>Budama (Pruning):<\/strong> A\u011fac\u0131n gereksiz dallar\u0131n\u0131 ve yaprak d\u00fc\u011f\u00fcmlerini kald\u0131rarak karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 azaltma i\u015flemidir.\n<ul>\n<li><strong>\u00d6n Budama (Pre-pruning):<\/strong> A\u011fa\u00e7 olu\u015fturulurken b\u00f6lme i\u015flemini durdurmak i\u00e7in uygulanan kriterlerdir. \u00d6rne\u011fin, bir d\u00fc\u011f\u00fcmdeki \u00f6rnek say\u0131s\u0131 belirli bir e\u015fi\u011fin alt\u0131na d\u00fc\u015ft\u00fc\u011f\u00fcnde veya bir b\u00f6lme safs\u0131zl\u0131kta yeterli iyile\u015fme sa\u011flamad\u0131\u011f\u0131nda b\u00f6lme durdurulur.<\/li>\n<li><strong>Son Budama (Post-pruning):<\/strong> A\u011fa\u00e7 tamamen olu\u015fturulduktan sonra, gereksiz dallar\u0131 ve d\u00fc\u011f\u00fcmleri kald\u0131rarak a\u011fac\u0131 basitle\u015ftirme i\u015flemidir. Bu genellikle bir do\u011frulama seti kullanarak yap\u0131l\u0131r.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Maksimum Derinlik (<code>max_depth<\/code>):<\/strong> A\u011fac\u0131n en fazla ka\u00e7 seviyeden olu\u015fabilece\u011fini belirleyen bir parametredir. A\u011fac\u0131n \u00e7ok derinle\u015fmesini engelleyerek a\u015f\u0131r\u0131 uyumu kontrol etmenin basit ve etkili bir yoludur.<\/li>\n<li><strong>Minimum \u00d6rnek Say\u0131s\u0131 (<code>min_samples_split<\/code>, <code>min_samples_leaf<\/code>):<\/strong>\n<ul>\n<li><code>min_samples_split<\/code>: Bir d\u00fc\u011f\u00fcm\u00fcn b\u00f6l\u00fcnebilmesi i\u00e7in o d\u00fc\u011f\u00fcmde bulunmas\u0131 gereken minimum \u00f6rnek say\u0131s\u0131n\u0131 belirler.<\/li>\n<li><code>min_samples_leaf<\/code>: Bir yaprak d\u00fc\u011f\u00fcm\u00fcnde bulunmas\u0131 gereken minimum \u00f6rnek say\u0131s\u0131n\u0131 belirler. Bu, \u00e7ok k\u00fc\u00e7\u00fck ve \u00f6nemsiz yaprak d\u00fc\u011f\u00fcmlerinin olu\u015fmas\u0131n\u0131 engeller.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Maksimum \u00d6zellik Say\u0131s\u0131 (<code>max_features<\/code>):<\/strong> Her b\u00f6lme noktas\u0131nda de\u011ferlendirilecek \u00f6zellik say\u0131s\u0131n\u0131 s\u0131n\u0131rlayarak modelin her zaman ayn\u0131 \u00f6zellikleri kullanmas\u0131n\u0131 engeller ve varyans\u0131 azalt\u0131r.<\/li>\n<\/ol>\n<div class=\"uzman-ipucu\">\n  Karar a\u011fa\u00e7lar\u0131 tek ba\u015f\u0131na a\u015f\u0131r\u0131 uyuma e\u011filimli olsa da, bu parametreleri dikkatli bir \u015fekilde ayarlayarak ve \u00e7apraz do\u011frulama (cross-validation) gibi teknikleri kullanarak modelinizin genelleme yetene\u011fini \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilirsiniz. Modelinizin e\u011fitim ve test setleri \u00fczerindeki performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak, a\u015f\u0131r\u0131 uyumu tespit etmenin ilk ad\u0131m\u0131d\u0131r.\n<\/div>\n<h3>A\u011fa\u00e7 Topluluklar\u0131 (Ensemble Methods): Daha G\u00fc\u00e7l\u00fc Modeller Yaratmak<\/h3>\n<p>A\u015f\u0131r\u0131 uyum dezavantaj\u0131n\u0131 dengelemek ve Karar A\u011fa\u00e7lar\u0131n\u0131n tahmin g\u00fcc\u00fcn\u00fc art\u0131rmak i\u00e7in \"topluluk \u00f6\u011frenmesi\" (ensemble learning) y\u00f6ntemleri geli\u015ftirilmi\u015ftir. Bu y\u00f6ntemler, birden fazla Karar A\u011fac\u0131n\u0131 bir araya getirerek daha g\u00fc\u00e7l\u00fc, daha kararl\u0131 ve daha do\u011fru bir model olu\u015fturmay\u0131 hedefler. En pop\u00fcler a\u011fa\u00e7 topluluklar\u0131 y\u00f6ntemleri \u015funlard\u0131r:<\/p>\n<ol>\n<li><strong>Rastgele Orman (Random Forest):<\/strong> Bu y\u00f6ntem, birden \u00e7ok Karar A\u011fac\u0131n\u0131 e\u011fitir ve her bir a\u011fa\u00e7, e\u011fitim verisinin rastgele se\u00e7ilmi\u015f bir alt k\u00fcmesi ve \u00f6zelliklerin rastgele se\u00e7ilmi\u015f bir alt k\u00fcmesi \u00fczerinde e\u011fitilir. Nihai tahmin, t\u00fcm a\u011fa\u00e7lar\u0131n tahminlerinin ortalamas\u0131 (regresyon i\u00e7in) veya \u00e7o\u011funluk oyu (s\u0131n\u0131fland\u0131rma i\u00e7in) al\u0131narak yap\u0131l\u0131r. Rastgelelik, her a\u011fac\u0131n farkl\u0131 bir perspektiften \u00f6\u011frenmesini sa\u011flar ve bu da modelin varyans\u0131n\u0131 (a\u015f\u0131r\u0131 uyum e\u011filimini) azalt\u0131r.<\/li>\n<li><strong>Gradyan Y\u00fckseltme (Gradient Boosting):<\/strong> Bu topluluk y\u00f6ntemi, zay\u0131f \u00f6\u011frenicileri (genellikle s\u0131\u011f Karar A\u011fa\u00e7lar\u0131) ard\u0131\u015f\u0131k olarak e\u011fiterek daha g\u00fc\u00e7l\u00fc bir model olu\u015fturur. Her yeni a\u011fa\u00e7, \u00f6nceki a\u011fac\u0131n yapt\u0131\u011f\u0131 hatalar\u0131 d\u00fczeltmeye odaklan\u0131r. Gradyan y\u00fckseltme, genellikle \u00e7ok y\u00fcksek do\u011fruluk oranlar\u0131na ula\u015fabilen g\u00fc\u00e7l\u00fc bir y\u00f6ntemdir, ancak a\u015f\u0131r\u0131 uyuma kar\u015f\u0131 daha dikkatli olunmas\u0131 gerekir. XGBoost, LightGBM ve CatBoost gibi pop\u00fcler k\u00fct\u00fcphaneler, Gradyan Y\u00fckseltme'nin geli\u015fmi\u015f uygulamalar\u0131d\u0131r.<\/li>\n<\/ol>\n<p>A\u011fa\u00e7 topluluklar\u0131, tek bir Karar A\u011fac\u0131n\u0131n zay\u0131fl\u0131klar\u0131n\u0131 gidermenin ve genel model performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rman\u0131n etkili bir yolunu sunar. Bu y\u00f6ntemler, g\u00fcn\u00fcm\u00fczde makine \u00f6\u011frenimi alan\u0131nda en \u00e7ok kullan\u0131lan ve en ba\u015far\u0131l\u0131 algoritmalar aras\u0131nda yer almaktad\u0131r.<\/p>\n<h2>Sonu\u00e7 ve S\u0131k\u00e7a Sorulan Sorular<\/h2>\n<p>Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131, makine \u00f6\u011frenimi d\u00fcnyas\u0131nda hem yeni ba\u015flayanlar i\u00e7in anla\u015f\u0131lmas\u0131 kolay hem de deneyimli veri bilimcileri i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7 sunar. Do\u011fal bir karar ak\u0131\u015f \u015femas\u0131 mant\u0131\u011f\u0131yla \u00e7al\u0131\u015fmas\u0131, onu son derece yorumlanabilir ve \u015feffaf k\u0131lar. Bu makalede, algoritman\u0131n temel prensiplerini, Gini Safs\u0131zl\u0131\u011f\u0131 ve Entropi gibi karar kriterlerini, Python ile nas\u0131l uygulanaca\u011f\u0131n\u0131 ve kredi risk de\u011ferlendirmesi gibi ger\u00e7ek d\u00fcnya senaryolar\u0131ndaki kullan\u0131m\u0131n\u0131 ele ald\u0131k. Ayr\u0131ca, a\u015f\u0131r\u0131 uyum gibi zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in budama teknikleri ve Rastgele Orman gibi a\u011fa\u00e7 topluluklar\u0131n\u0131n \u00f6nemine de\u011findik.<\/p>\n<p>Karar a\u011fa\u00e7lar\u0131, karma\u015f\u0131k verileri anla\u015f\u0131l\u0131r kurallara d\u00f6n\u00fc\u015ft\u00fcrme yetene\u011fi sayesinde i\u015f kararlar\u0131n\u0131n al\u0131nmas\u0131nda, m\u00fc\u015fteri davran\u0131\u015flar\u0131n\u0131n analiz edilmesinde, hastal\u0131k te\u015fhisinde ve daha bir\u00e7ok alanda de\u011ferli i\u00e7g\u00f6r\u00fcler sa\u011flar. Tek ba\u015f\u0131na kullan\u0131ld\u0131\u011f\u0131nda baz\u0131 s\u0131n\u0131rlamalara sahip olsa da, topluluk \u00f6\u011frenmesi teknikleriyle birle\u015ftirildi\u011finde, g\u00fcn\u00fcm\u00fcz\u00fcn en etkili ve yayg\u0131n kullan\u0131lan makine \u00f6\u011frenimi modellerinden biri haline gelmektedir. Umar\u0131z bu rehber, Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131'n\u0131 temelden ileri seviyeye kadar anlaman\u0131za yard\u0131mc\u0131 olmu\u015ftur.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<ol>\n<li>\n<h4>Karar A\u011fa\u00e7lar\u0131 sadece s\u0131n\u0131fland\u0131rma i\u00e7in mi kullan\u0131l\u0131r?<\/h4>\n<p>Hay\u0131r, Karar A\u011fa\u00e7lar\u0131 hem s\u0131n\u0131fland\u0131rma (\u00f6rne\u011fin, bir m\u00fc\u015fterinin churn edip etmeyece\u011fini tahmin etmek) hem de regresyon (\u00f6rne\u011fin, bir evin fiyat\u0131n\u0131 tahmin etmek) problemleri i\u00e7in kullan\u0131labilir. Regresyon a\u011fa\u00e7lar\u0131nda, yaprak d\u00fc\u011f\u00fcmler o d\u00fc\u011f\u00fcme d\u00fc\u015fen \u00f6rneklerin ortalama de\u011ferini verirken, s\u0131n\u0131fland\u0131rma a\u011fa\u00e7lar\u0131nda \u00e7o\u011funluk s\u0131n\u0131f\u0131n\u0131 verir.<\/p>\n<\/li>\n<li>\n<h4>Karar A\u011fa\u00e7lar\u0131 neden a\u015f\u0131r\u0131 uyum (overfitting) e\u011filimindedir?<\/h4>\n<p>Karar A\u011fa\u00e7lar\u0131, e\u011fitim verisindeki her detay\u0131 ve hatta g\u00fcr\u00fclt\u00fcy\u00fc bile \u00f6\u011frenmeye meyillidir, \u00e7\u00fcnk\u00fc b\u00f6l\u00fcnme s\u00fcreci saf d\u00fc\u011f\u00fcmler elde edene kadar devam edebilir. Bu durum, a\u011fac\u0131n \u00e7ok derin ve karma\u015f\u0131k hale gelmesine neden olur ve yeni, g\u00f6r\u00fcnmeyen veriler \u00fczerinde genelleme yetene\u011fini kaybetmesine yol a\u00e7ar.<\/p>\n<\/li>\n<li>\n<h4>Gini Safs\u0131zl\u0131\u011f\u0131 ile Entropi aras\u0131nda ne fark vard\u0131r? Hangisini kullanmal\u0131y\u0131m?<\/h4>\n<p>Her ikisi de bir d\u00fc\u011f\u00fcmdeki veri setinin safl\u0131\u011f\u0131n\u0131 \u00f6l\u00e7mek i\u00e7in kullan\u0131lan metriklerdir. Gini Safs\u0131zl\u0131\u011f\u0131, rastgele se\u00e7ilen iki \u00f6\u011fenin farkl\u0131 s\u0131n\u0131flarda olma olas\u0131l\u0131\u011f\u0131n\u0131 \u00f6l\u00e7erken, Entropi bir d\u00fc\u011f\u00fcmdeki bilginin belirsizli\u011fini veya d\u00fczensizli\u011fini \u00f6l\u00e7er. Genellikle birbirlerine \u00e7ok benzer sonu\u00e7lar verirler. Gini daha h\u0131zl\u0131 hesaplan\u0131r \u00e7\u00fcnk\u00fc logaritmik hesaplamalar i\u00e7ermezken, Entropi biraz daha fazla hesaplama gerektirebilir. \u00c7o\u011fu durumda, hangisini se\u00e7ti\u011finiz b\u00fcy\u00fck bir fark yaratmaz; deneme yan\u0131lma yoluyla veri setiniz i\u00e7in en iyi performans\u0131 vereni se\u00e7ebilirsiniz.<\/p>\n<\/li>\n<li>\n<h4>Karar A\u011fa\u00e7lar\u0131n\u0131n karar verme s\u00fcrecini g\u00f6rselle\u015ftirmek m\u00fcmk\u00fcn m\u00fcd\u00fcr?<\/h4>\n<p>Evet, bu Karar A\u011fa\u00e7lar\u0131n\u0131n en b\u00fcy\u00fck avantajlar\u0131ndan biridir. <code>scikit-learn<\/code> k\u00fct\u00fcphanesi ile e\u011fitilmi\u015f bir Karar A\u011fac\u0131n\u0131 <code>export_graphviz<\/code> fonksiyonunu kullanarak DOT format\u0131na aktarabilir ve bu format\u0131 Graphviz gibi ara\u00e7larla g\u00f6rselle\u015ftirebilirsiniz. Bu, a\u011fac\u0131n her bir d\u00fc\u011f\u00fcmdeki ko\u015fullar\u0131 ve b\u00f6lmeleri net bir \u015fekilde g\u00f6rmenizi sa\u011flar.<\/p>\n<\/li>\n<li>\n<h4>Karar A\u011fa\u00e7lar\u0131 ne zaman di\u011fer algoritmalara tercih edilmelidir?<\/h4>\n<p>Karar A\u011fa\u00e7lar\u0131, \u00f6zellikle modelin yorumlanabilirli\u011finin ve \u015feffafl\u0131\u011f\u0131n\u0131n kritik oldu\u011fu durumlarda (\u00f6rne\u011fin, i\u015f kararlar\u0131n\u0131 a\u00e7\u0131klamak, yasal uyumluluk sa\u011flamak) tercih edilir. Ayr\u0131ca, veri setindeki do\u011frusal olmayan ili\u015fkileri kolayca yakalayabilirler. Tek ba\u015f\u0131na a\u015f\u0131r\u0131 uyuma e\u011filimli olsalar da, topluluk y\u00f6ntemleriyle (Random Forest, Gradient Boosting) birle\u015ftirildiklerinde, y\u00fcksek performansl\u0131 ve g\u00fc\u00e7l\u00fc modeller olu\u015fturarak bir\u00e7ok karma\u015f\u0131k probleme \u00e7\u00f6z\u00fcm sunarlar.<\/p>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"B\u00fcy\u00fck veri k\u00fcmeleriyle kar\u015f\u0131la\u015ft\u0131\u011f\u0131m\u0131zda, bu y\u0131\u011f\u0131nlar\u0131n i\u00e7erisinde gizlenmi\u015f de\u011ferli bilgileri ve karar alma kal\u0131plar\u0131n\u0131 nas\u0131l ortaya \u00e7\u0131karabiliriz? \u0130\u015fte&hellip;","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-32189","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.5 (Yoast SEO v25.3.1) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131: Temelden \u0130leri Seviyeye Kapsaml\u0131 Rehber<\/title>\n<meta name=\"description\" content=\"B\u00fcy\u00fck veri k\u00fcmeleriyle kar\u015f\u0131la\u015ft\u0131\u011f\u0131m\u0131zda, bu y\u0131\u011f\u0131nlar\u0131n i\u00e7erisinde gizlenmi\u015f de\u011ferli bilgileri ve karar alma kal\u0131plar\u0131n\u0131 nas\u0131l ortaya \u00e7\u0131karabiliriz? \u0130\u015fte tam bu noktada, Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131 devreye giriyor. Makine \u00f6\u011freniminin en sezgisel ve g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131ndan biri olan karar a\u011fa\u00e7lar\u0131, karma\u015f\u0131k verileri anla\u015f\u0131l\u0131r bir yap\u0131ya d\u00f6n\u00fc\u015ft\u00fcrerek tahmin yapmam\u0131z\u0131 ve karar alma s\u00fcre\u00e7lerini optimize etmemizi sa\u011flar. Bu kapsaml\u0131 rehberde, karar a\u011fa\u00e7lar\u0131n\u0131n temel prensiplerinden ileri d\u00fczey uygulamalar\u0131na kadar her \u015feyi ke\u015ffedecek, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle bu b\u00fcy\u00fcleyici algoritmay\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131: Temelden \u0130leri Seviyeye Kapsaml\u0131 Rehber\" \/>\n<meta property=\"og:description\" content=\"B\u00fcy\u00fck veri k\u00fcmeleriyle kar\u015f\u0131la\u015ft\u0131\u011f\u0131m\u0131zda, bu y\u0131\u011f\u0131nlar\u0131n i\u00e7erisinde gizlenmi\u015f de\u011ferli bilgileri ve karar alma kal\u0131plar\u0131n\u0131 nas\u0131l ortaya \u00e7\u0131karabiliriz? \u0130\u015fte tam bu noktada, Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131 devreye giriyor. Makine \u00f6\u011freniminin en sezgisel ve g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131ndan biri olan karar a\u011fa\u00e7lar\u0131, karma\u015f\u0131k verileri anla\u015f\u0131l\u0131r bir yap\u0131ya d\u00f6n\u00fc\u015ft\u00fcrerek tahmin yapmam\u0131z\u0131 ve karar alma s\u00fcre\u00e7lerini optimize etmemizi sa\u011flar. Bu kapsaml\u0131 rehberde, karar a\u011fa\u00e7lar\u0131n\u0131n temel prensiplerinden ileri d\u00fczey uygulamalar\u0131na kadar her \u015feyi ke\u015ffedecek, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle bu b\u00fcy\u00fcleyici algoritmay\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-10-18T17:31:19+00:00\" \/>\n<meta name=\"author\" content=\"Fatih Soysal\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Fatih Soysal\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"25 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131: Temelden \u0130leri Seviyeye Kapsaml\u0131 Rehber\",\"datePublished\":\"2025-10-18T17:31:19+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/\"},\"wordCount\":4647,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/\",\"name\":\"Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131: Temelden \u0130leri Seviyeye Kapsaml\u0131 Rehber\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-10-18T17:31:19+00:00\",\"description\":\"B\u00fcy\u00fck veri k\u00fcmeleriyle kar\u015f\u0131la\u015ft\u0131\u011f\u0131m\u0131zda, bu y\u0131\u011f\u0131nlar\u0131n i\u00e7erisinde gizlenmi\u015f de\u011ferli bilgileri ve karar alma kal\u0131plar\u0131n\u0131 nas\u0131l ortaya \u00e7\u0131karabiliriz? \u0130\u015fte tam bu noktada, Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131 devreye giriyor. 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Bu kapsaml\u0131 rehberde, karar a\u011fa\u00e7lar\u0131n\u0131n temel prensiplerinden ileri d\u00fczey uygulamalar\u0131na kadar her \u015feyi ke\u015ffedecek, ger\u00e7ek d\u00fcnya \u00f6rnekleriyle bu b\u00fcy\u00fcleyici algoritmay\u0131 ad\u0131m ad\u0131m \u00f6\u011freneceksiniz.\",\"breadcrumb\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/karar-agaclari-algoritmasi-temelden-ileri-seviyeye-kapsamli-rehber\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Anasayfa\",\"item\":\"https:\/\/fatihsoysal.com\/blog\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131: Temelden \u0130leri Seviyeye Kapsaml\u0131 Rehber\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/\",\"name\":\"Fatihsoysal.com\",\"description\":\"Blog - Yaz\u0131l\u0131m D\u00fcnyas\u0131 Tecr\u00fcbelerim\",\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/fatihsoysal.com\/blog\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"tr\"},{\"@type\":[\"Person\",\"Organization\"],\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\",\"name\":\"Fatih Soysal\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"contentUrl\":\"https:\/\/fatihsoysal.com\/blog\/wp-content\/uploads\/2024\/04\/cropped-replicate-prediction-3kgg1hgjn5rgp0cf0p5tr0jw7w-1.png\",\"width\":512,\"height\":512,\"caption\":\"Fatih Soysal\"},\"logo\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/image\/\"},\"description\":\"Kullan\u0131m ve kodlama m\u00fckemmeliyetini odak alan uygulamalar olu\u015fturma deneyimine sahip, profesyonel olarak 15+ y\u0131l \u00fczeri deneyime sahip bir yaz\u0131l\u0131m m\u00fchendisi.\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/author\/fatihsoysal\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131: Temelden \u0130leri Seviyeye Kapsaml\u0131 Rehber","description":"B\u00fcy\u00fck veri k\u00fcmeleriyle kar\u015f\u0131la\u015ft\u0131\u011f\u0131m\u0131zda, bu y\u0131\u011f\u0131nlar\u0131n i\u00e7erisinde gizlenmi\u015f de\u011ferli bilgileri ve karar alma kal\u0131plar\u0131n\u0131 nas\u0131l ortaya \u00e7\u0131karabiliriz? \u0130\u015fte tam bu noktada, Karar A\u011fa\u00e7lar\u0131 Algoritmas\u0131 devreye giriyor. Makine \u00f6\u011freniminin en sezgisel ve g\u00fc\u00e7l\u00fc ara\u00e7lar\u0131ndan biri olan karar a\u011fa\u00e7lar\u0131, karma\u015f\u0131k verileri anla\u015f\u0131l\u0131r bir yap\u0131ya d\u00f6n\u00fc\u015ft\u00fcrerek tahmin yapmam\u0131z\u0131 ve karar alma s\u00fcre\u00e7lerini optimize etmemizi sa\u011flar. 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