Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)<\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<p>Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)<\/p>\n<p>Makine \u00f6\u011frenmesi modellerinin ba\u015far\u0131s\u0131n\u0131 etkileyen en \u00f6nemli fakt\u00f6rlerden ikisi, a\u015f\u0131r\u0131 uyum (overfitting) ve az uyum (underfitting)’dir. Bu iki kavram, modelin e\u011fitim verilerine ne kadar iyi uydu\u011funu ve yeni, g\u00f6r\u00fclmemi\u015f verilere ne kadar iyi genelle\u015ftirilebildi\u011fini a\u00e7\u0131klar. Ba\u015fka bir deyi\u015fle, modelin \u00f6\u011frenme ba\u015far\u0131s\u0131 bu iki durum aras\u0131nda ince bir \u00e7izgi \u00fczerinde y\u00fcr\u00fcr.<\/p>\n<h2>Az Uyum (Underfitting): Basit Modellerin S\u0131n\u0131r\u0131<\/h2>\n<p>Az uyum, modelin e\u011fitim verilerindeki kal\u0131plar\u0131 yeterince yakalayamad\u0131\u011f\u0131, dolay\u0131s\u0131yla hem e\u011fitim hem de test verilerinde d\u00fc\u015f\u00fck performans g\u00f6sterdi\u011fi durumdur. Bu durum genellikle \u00e7ok basit bir model kullan\u0131ld\u0131\u011f\u0131nda ortaya \u00e7\u0131kar. \u00d6rne\u011fin, do\u011frusal bir model karma\u015f\u0131k veriler i\u00e7in yeterli olmayabilir ve d\u00fc\u015f\u00fck do\u011frulukla sonu\u00e7lanabilir. Bu gibi durumlarda modelin karma\u015f\u0131kl\u0131\u011f\u0131 art\u0131r\u0131lmal\u0131, daha fazla \u00f6zellik eklenmeli veya daha g\u00fc\u00e7l\u00fc bir algoritma tercih edilmelidir. Sonu\u00e7 olarak, modelin daha fazla veriyi \u00f6\u011frenmesi ve daha iyi genelleme yapmas\u0131 sa\u011flan\u0131r.<\/p>\n<p><strong>Az uyumun belirtileri \u015funlard\u0131r:<\/strong> Hem e\u011fitim hem de test verilerinde d\u00fc\u015f\u00fck do\u011fruluk oranlar\u0131 g\u00f6zlenir. Modelin karma\u015f\u0131kl\u0131\u011f\u0131 art\u0131r\u0131larak iyile\u015ftirme yap\u0131labilir.<\/p>\n<h2>A\u015f\u0131r\u0131 Uyum (Overfitting): \u00c7ok Detayl\u0131 \u00d6\u011frenme<\/h2>\n<p>A\u015f\u0131r\u0131 uyum ise tam tersine, modelin e\u011fitim verilerine a\u015f\u0131r\u0131 derecede uyum sa\u011flad\u0131\u011f\u0131, ancak yeni verilere genelleme yapamad\u0131\u011f\u0131 bir durumdur. Model, e\u011fitim verilerindeki g\u00fcr\u00fclt\u00fcy\u00fc ve rastgele varyasyonlar\u0131 bile \u00f6\u011frenir. Bu da, e\u011fitim verilerinde y\u00fcksek do\u011fruluk, test verilerinde ise d\u00fc\u015f\u00fck do\u011frulukla sonu\u00e7lan\u0131r. A\u015f\u0131r\u0131 uyum genellikle \u00e7ok karma\u015f\u0131k modellerde, fazla say\u0131da \u00f6zellik kullan\u0131ld\u0131\u011f\u0131nda veya d\u00fczenleme eksikli\u011finde ortaya \u00e7\u0131kar. Bu sorunu \u00e7\u00f6zmek i\u00e7in modelin basitle\u015ftirilmesi, \u00f6zellik se\u00e7imi yap\u0131lmas\u0131 veya d\u00fczenleme teknikleri (\u00f6rne\u011fin, L1 veya L2 d\u00fczenlemesi) kullan\u0131lmas\u0131 gerekir.<\/p>\n<p><strong>A\u015f\u0131r\u0131 uyumun belirtileri \u015funlard\u0131r:<\/strong> E\u011fitim verilerinde \u00e7ok y\u00fcksek, test verilerinde ise d\u00fc\u015f\u00fck do\u011fruluk oranlar\u0131 g\u00f6r\u00fcl\u00fcr. Modelin karma\u015f\u0131kl\u0131\u011f\u0131 azalt\u0131larak veya d\u00fczenleme teknikleri kullan\u0131larak iyile\u015ftirme yap\u0131labilir. \u00d6rne\u011fin, daha az say\u0131da d\u00fc\u011f\u00fcm i\u00e7eren bir sinir a\u011f\u0131 kullan\u0131labilir veya karar a\u011fa\u00e7lar\u0131nda budama yap\u0131labilir.<\/p>\n<h2>Overfitting ve Underfitting’in G\u00f6rsel Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h2>\n<p><img decoding=\"async\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/thumb\/1\/16\/Overfitting.svg\/1280px-Overfitting.svg.png\" alt=\"Overfitting ve Underfitting Kar\u015f\u0131la\u015ft\u0131rmas\u0131\" width=\"500\"><\/p>\n<p>Yukar\u0131daki g\u00f6rsel, a\u015f\u0131r\u0131 uyum ve az uyumun g\u00f6rsel bir temsilini sunmaktad\u0131r. G\u00f6r\u00fcld\u00fc\u011f\u00fc \u00fczere, az uyumlu model verileri iyi temsil edemezken, a\u015f\u0131r\u0131 uyumlu model verilerin detay\u0131na gere\u011finden fazla odaklanm\u0131\u015f ve genelleme yetene\u011fini kaybetmi\u015ftir.<\/p>\n<h2>\u00c7\u00f6z\u00fcm Yollar\u0131 ve \u0130pu\u00e7lar\u0131<\/h2>\n<p>Hem a\u015f\u0131r\u0131 uyum hem de az uyum sorunlar\u0131n\u0131 \u00e7\u00f6zmek i\u00e7in \u00e7e\u015fitli y\u00f6ntemler kullan\u0131labilir. Bunlar aras\u0131nda veri art\u0131rma, \u00f6zellik se\u00e7imi, model basitle\u015ftirme, d\u00fczenleme teknikleri ve \u00e7apraz do\u011frulama yer al\u0131r. Do\u011fru y\u00f6ntemi se\u00e7mek, veri setinin \u00f6zelliklerine ve modelin karma\u015f\u0131kl\u0131\u011f\u0131na ba\u011fl\u0131d\u0131r. \u00d6rne\u011fin, k\u00fc\u00e7\u00fck bir veri setinde veri art\u0131rma teknikleri olduk\u00e7a faydal\u0131 olabilirken, b\u00fcy\u00fck veri setlerinde \u00f6zellik se\u00e7imi daha etkili olabilir.<\/p>\n<p>Ayr\u0131ca, modelin performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in farkl\u0131 metrikler kullanmak da \u00f6nemlidir. Sadece do\u011fruluk oran\u0131na bakmak yeterli olmayabilir. Hassasiyet, duyarl\u0131l\u0131k, F1 skoru gibi metrikler de dikkate al\u0131nmal\u0131d\u0131r. Bu metrikler, modelin performans\u0131n\u0131 daha kapsaml\u0131 bir \u015fekilde de\u011ferlendirmeye yard\u0131mc\u0131 olur.<\/p>\n<p>Sonu\u00e7 olarak, ba\u015far\u0131l\u0131 bir makine \u00f6\u011frenmesi modeli geli\u015ftirmek i\u00e7in, a\u015f\u0131r\u0131 uyum ve az uyum aras\u0131nda hassas bir denge kurmak \u00e7ok \u00f6nemlidir. Bu dengeyi kurmak, do\u011fru model se\u00e7imini, veri \u00f6n i\u015fleme tekniklerini ve model de\u011ferlendirme metriklerini do\u011fru bir \u015fekilde kullanmay\u0131 gerektirir.<\/p>\n<p>Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> adresini ziyaret edebilirsiniz.<\/p>\n<p>Faydal\u0131 bulabilece\u011finiz di\u011fer kaynaklar:<\/p>\n<ul>\n<li><a href=\"https:\/\/dev.to\/hyperkai\/overfitting-vs-underfitting-33i8\">Dev.to Overfitting vs Underfitting Makalesi (\u0130ngilizce)<\/a><\/li>\n<\/ul>\n<p>#Etiketler: Overfitting, Underfitting, Makine \u00d6\u011frenmesi, Model Se\u00e7imi, Veri Analizi, Genelleme, D\u00fczenleme, \u00d6zellik Se\u00e7imi, Do\u011fruluk, Makine \u00d6\u011frenmesi Modelleri, Veri Bilimci, Yapay Zeka<\/p>\n<p><\/body><br \/>\n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting) Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)…","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-4170","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesi modellerinin ba\u015far\u0131s\u0131n\u0131 etkileyen en \u00f6nemli fakt\u00f6rlerden ikisi, a\u015f\u0131r\u0131 uyum (overfitting) ve az uyum (underfitting)'dir. Bu iki kavram, modelin e\u011fitim verilerine ne kadar iyi uydu\u011funu ve yeni, g\u00f6r\u00fclmemi\u015f verilere ne kadar iyi genelle\u015ftirilebildi\u011fini a\u00e7\u0131klar. 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{"id":4170,"date":"2024-11-18T02:00:18","date_gmt":"2024-11-17T23:00:18","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesinde-asiri-uyum-overfitting-ve-az-uyum-underfitting\/"},"modified":"2024-11-18T02:00:18","modified_gmt":"2024-11-17T23:00:18","slug":"makine-ogrenmesinde-asiri-uyum-overfitting-ve-az-uyum-underfitting","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesinde-asiri-uyum-overfitting-ve-az-uyum-underfitting\/","title":{"rendered":"Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)"},"content":{"rendered":"

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\n Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)<\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<p>Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)<\/p>\n<p>Makine \u00f6\u011frenmesi modellerinin ba\u015far\u0131s\u0131n\u0131 etkileyen en \u00f6nemli fakt\u00f6rlerden ikisi, a\u015f\u0131r\u0131 uyum (overfitting) ve az uyum (underfitting)’dir. Bu iki kavram, modelin e\u011fitim verilerine ne kadar iyi uydu\u011funu ve yeni, g\u00f6r\u00fclmemi\u015f verilere ne kadar iyi genelle\u015ftirilebildi\u011fini a\u00e7\u0131klar. Ba\u015fka bir deyi\u015fle, modelin \u00f6\u011frenme ba\u015far\u0131s\u0131 bu iki durum aras\u0131nda ince bir \u00e7izgi \u00fczerinde y\u00fcr\u00fcr.<\/p>\n<h2>Az Uyum (Underfitting): Basit Modellerin S\u0131n\u0131r\u0131<\/h2>\n<p>Az uyum, modelin e\u011fitim verilerindeki kal\u0131plar\u0131 yeterince yakalayamad\u0131\u011f\u0131, dolay\u0131s\u0131yla hem e\u011fitim hem de test verilerinde d\u00fc\u015f\u00fck performans g\u00f6sterdi\u011fi durumdur. Bu durum genellikle \u00e7ok basit bir model kullan\u0131ld\u0131\u011f\u0131nda ortaya \u00e7\u0131kar. \u00d6rne\u011fin, do\u011frusal bir model karma\u015f\u0131k veriler i\u00e7in yeterli olmayabilir ve d\u00fc\u015f\u00fck do\u011frulukla sonu\u00e7lanabilir. Bu gibi durumlarda modelin karma\u015f\u0131kl\u0131\u011f\u0131 art\u0131r\u0131lmal\u0131, daha fazla \u00f6zellik eklenmeli veya daha g\u00fc\u00e7l\u00fc bir algoritma tercih edilmelidir. Sonu\u00e7 olarak, modelin daha fazla veriyi \u00f6\u011frenmesi ve daha iyi genelleme yapmas\u0131 sa\u011flan\u0131r.<\/p>\n<p><strong>Az uyumun belirtileri \u015funlard\u0131r:<\/strong> Hem e\u011fitim hem de test verilerinde d\u00fc\u015f\u00fck do\u011fruluk oranlar\u0131 g\u00f6zlenir. Modelin karma\u015f\u0131kl\u0131\u011f\u0131 art\u0131r\u0131larak iyile\u015ftirme yap\u0131labilir.<\/p>\n<h2>A\u015f\u0131r\u0131 Uyum (Overfitting): \u00c7ok Detayl\u0131 \u00d6\u011frenme<\/h2>\n<p>A\u015f\u0131r\u0131 uyum ise tam tersine, modelin e\u011fitim verilerine a\u015f\u0131r\u0131 derecede uyum sa\u011flad\u0131\u011f\u0131, ancak yeni verilere genelleme yapamad\u0131\u011f\u0131 bir durumdur. Model, e\u011fitim verilerindeki g\u00fcr\u00fclt\u00fcy\u00fc ve rastgele varyasyonlar\u0131 bile \u00f6\u011frenir. Bu da, e\u011fitim verilerinde y\u00fcksek do\u011fruluk, test verilerinde ise d\u00fc\u015f\u00fck do\u011frulukla sonu\u00e7lan\u0131r. A\u015f\u0131r\u0131 uyum genellikle \u00e7ok karma\u015f\u0131k modellerde, fazla say\u0131da \u00f6zellik kullan\u0131ld\u0131\u011f\u0131nda veya d\u00fczenleme eksikli\u011finde ortaya \u00e7\u0131kar. Bu sorunu \u00e7\u00f6zmek i\u00e7in modelin basitle\u015ftirilmesi, \u00f6zellik se\u00e7imi yap\u0131lmas\u0131 veya d\u00fczenleme teknikleri (\u00f6rne\u011fin, L1 veya L2 d\u00fczenlemesi) kullan\u0131lmas\u0131 gerekir.<\/p>\n<p><strong>A\u015f\u0131r\u0131 uyumun belirtileri \u015funlard\u0131r:<\/strong> E\u011fitim verilerinde \u00e7ok y\u00fcksek, test verilerinde ise d\u00fc\u015f\u00fck do\u011fruluk oranlar\u0131 g\u00f6r\u00fcl\u00fcr. Modelin karma\u015f\u0131kl\u0131\u011f\u0131 azalt\u0131larak veya d\u00fczenleme teknikleri kullan\u0131larak iyile\u015ftirme yap\u0131labilir. \u00d6rne\u011fin, daha az say\u0131da d\u00fc\u011f\u00fcm i\u00e7eren bir sinir a\u011f\u0131 kullan\u0131labilir veya karar a\u011fa\u00e7lar\u0131nda budama yap\u0131labilir.<\/p>\n<h2>Overfitting ve Underfitting’in G\u00f6rsel Kar\u015f\u0131la\u015ft\u0131rmas\u0131<\/h2>\n<p><img decoding=\"async\" src=\"https:\/\/upload.wikimedia.org\/wikipedia\/commons\/thumb\/1\/16\/Overfitting.svg\/1280px-Overfitting.svg.png\" alt=\"Overfitting ve Underfitting Kar\u015f\u0131la\u015ft\u0131rmas\u0131\" width=\"500\"><\/p>\n<p>Yukar\u0131daki g\u00f6rsel, a\u015f\u0131r\u0131 uyum ve az uyumun g\u00f6rsel bir temsilini sunmaktad\u0131r. G\u00f6r\u00fcld\u00fc\u011f\u00fc \u00fczere, az uyumlu model verileri iyi temsil edemezken, a\u015f\u0131r\u0131 uyumlu model verilerin detay\u0131na gere\u011finden fazla odaklanm\u0131\u015f ve genelleme yetene\u011fini kaybetmi\u015ftir.<\/p>\n<h2>\u00c7\u00f6z\u00fcm Yollar\u0131 ve \u0130pu\u00e7lar\u0131<\/h2>\n<p>Hem a\u015f\u0131r\u0131 uyum hem de az uyum sorunlar\u0131n\u0131 \u00e7\u00f6zmek i\u00e7in \u00e7e\u015fitli y\u00f6ntemler kullan\u0131labilir. Bunlar aras\u0131nda veri art\u0131rma, \u00f6zellik se\u00e7imi, model basitle\u015ftirme, d\u00fczenleme teknikleri ve \u00e7apraz do\u011frulama yer al\u0131r. Do\u011fru y\u00f6ntemi se\u00e7mek, veri setinin \u00f6zelliklerine ve modelin karma\u015f\u0131kl\u0131\u011f\u0131na ba\u011fl\u0131d\u0131r. \u00d6rne\u011fin, k\u00fc\u00e7\u00fck bir veri setinde veri art\u0131rma teknikleri olduk\u00e7a faydal\u0131 olabilirken, b\u00fcy\u00fck veri setlerinde \u00f6zellik se\u00e7imi daha etkili olabilir.<\/p>\n<p>Ayr\u0131ca, modelin performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in farkl\u0131 metrikler kullanmak da \u00f6nemlidir. Sadece do\u011fruluk oran\u0131na bakmak yeterli olmayabilir. Hassasiyet, duyarl\u0131l\u0131k, F1 skoru gibi metrikler de dikkate al\u0131nmal\u0131d\u0131r. Bu metrikler, modelin performans\u0131n\u0131 daha kapsaml\u0131 bir \u015fekilde de\u011ferlendirmeye yard\u0131mc\u0131 olur.<\/p>\n<p>Sonu\u00e7 olarak, ba\u015far\u0131l\u0131 bir makine \u00f6\u011frenmesi modeli geli\u015ftirmek i\u00e7in, a\u015f\u0131r\u0131 uyum ve az uyum aras\u0131nda hassas bir denge kurmak \u00e7ok \u00f6nemlidir. Bu dengeyi kurmak, do\u011fru model se\u00e7imini, veri \u00f6n i\u015fleme tekniklerini ve model de\u011ferlendirme metriklerini do\u011fru bir \u015fekilde kullanmay\u0131 gerektirir.<\/p>\n<p>Daha fazla bilgi i\u00e7in <a href=\"https:\/\/fatihsoysal.com\">fatihsoysal.com<\/a> adresini ziyaret edebilirsiniz.<\/p>\n<p>Faydal\u0131 bulabilece\u011finiz di\u011fer kaynaklar:<\/p>\n<ul>\n<li><a href=\"https:\/\/dev.to\/hyperkai\/overfitting-vs-underfitting-33i8\">Dev.to Overfitting vs Underfitting Makalesi (\u0130ngilizce)<\/a><\/li>\n<\/ul>\n<p>#Etiketler: Overfitting, Underfitting, Makine \u00d6\u011frenmesi, Model Se\u00e7imi, Veri Analizi, Genelleme, D\u00fczenleme, \u00d6zellik Se\u00e7imi, Do\u011fruluk, Makine \u00d6\u011frenmesi Modelleri, Veri Bilimci, Yapay Zeka<\/p>\n<p><\/body><br \/>\n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting) Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)…","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-4170","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n<title>Makine \u00d6\u011frenmesinde A\u015f\u0131r\u0131 Uyum (Overfitting) ve Az Uyum (Underfitting)<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesi modellerinin ba\u015far\u0131s\u0131n\u0131 etkileyen en \u00f6nemli fakt\u00f6rlerden ikisi, a\u015f\u0131r\u0131 uyum (overfitting) ve az uyum (underfitting)'dir. Bu iki kavram, modelin e\u011fitim verilerine ne kadar iyi uydu\u011funu ve yeni, g\u00f6r\u00fclmemi\u015f verilere ne kadar iyi genelle\u015ftirilebildi\u011fini a\u00e7\u0131klar. 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