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Eğitmen, assemble tekniklerini detaylı şekilde anlatmaktadır.","Video, assemble tekniklerinin ne olduğunu açıklayarak başlıyor ve oylama ile sınıflandırma, bagging ve boosting kavramlarını ele alıyor. İlk bölümde random forest ve gradient boosting classifier gibi popüler assemble modellerinin Python kodları üzerinden mouse, blobs ve göğüs kanseri veri setleri üzerinde uygulamaları gösterilirken, ikinci bölümde modelin eğitim ve test verisi üzerindeki performansının nasıl değerlendirileceği, aşırı uydurma sorunu ve learning rate hiper parametresinin nasıl ayarlanacağı anlatılıyor.","Eğitim içeriğinde modelin doğruluk oranlarının score metodu kullanılarak nasıl gösterileceği ve modelin kompleksliğini azaltmak için learning rate'in 0.1 değerine nasıl ayarlanacağı adım adım gösterilmektedir."]},"endTime":864,"title":"Makine Öğrenmesi'nde Assemble Teknikleri Eğitimi","beginTime":0}],"fullResult":[{"index":0,"title":"Assemble Tekniği Tanıtımı","list":{"type":"unordered","items":["Bu derste assemble konusu, oylama ile sınıflandırma, bagging ve boosting kavramları, random forest ve gradient boosting metotları anlatılacak.","Karar ağaçları tekniğinin overfitting (modelin aşırı uydurulmaya meyilli olması) etkisini azaltmak için çoklu overfitting tahmincileri kombine edilebilir.","Kalabalığın zekası prensibi, bir grup kestiricinin tahminlerini toplayarak en iyi kestiricinin daha iyi tahmini yapabilmesini sağlar."]},"beginTime":1,"endTime":85,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=1&ask_summarization=1"},{"index":1,"title":"Random Forest","list":{"type":"unordered","items":["Karar ağaçlarının assemble'a runner forest denir ve her bir ağacın tahmininden en fazla oy alan sınıf tahmini olarak alınır.","Bu teknik basitliğine rağmen bugün en güçlü makine öğrenmesi algoritmalarından biridir.","Tahmincilerin her biri diğerinden bağımsız olarak çalıştığı zaman assemble metotları daha iyi çalışır, bu nedenle birbirinden farklı algoritmalar kullanılması tavsiye edilir."]},"beginTime":85,"endTime":147,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=85&ask_summarization=1"},{"index":2,"title":"Assemble Örneği","list":{"type":"unordered","items":["Movese veri seti kullanılarak assemble teknikleri gösterilecektir.","Veri seti parçalanıp, logistic regresyon, destek vektör makineleri, random forest ve wating sınıflayıcıları import edilerek modeller kurulmuştur.","Oylama ile oluşturulan modelin doğruluk değeri diğer modellerin doğruluk değerine eşit veya daha yüksek çıkmıştır."]},"beginTime":147,"endTime":304,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=147&ask_summarization=1"},{"index":3,"title":"Soft Wating","list":{"type":"unordered","items":["Sınıflandırıcıların sınıf tahminlerinin ortalaması alınarak tahmin yapıldığında, her bir sınıflayıcının sınıf olasılıkları hesaplanırsa daha yüksek tahmin yapılır.","Bu opsiyona soft wating denir ve her bir tahmincinin sınıflama tahminlerini hesaplayıp wating argümanına soft yazmak yeterlidir."]},"beginTime":304,"endTime":334,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=304&ask_summarization=1"},{"index":4,"title":"Bagging ve Boosting","list":{"type":"unordered","items":["Bagging, her tahminci için aynı eğitim algoritmasını kullanmak ama bu tahmincileri eğitirken eğitim verilerinin farklı alt kümelerini kullanmaktır.","Örneklem yerine koymalı seçiliyorsa bu metoda bagging denir, örnekten yerine koyması seçilirse buna peasting denir.","Bu teknik ile her bir kestirici orijinal veri setinde eğitildiğinden daha fazla yanlılığa sahiptir, ama bir araya geldiklerinde varyans ve yanlılık azalır."]},"beginTime":334,"endTime":397,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=334&ask_summarization=1"},{"index":5,"title":"Bagging Örneği","list":{"type":"unordered","items":["Bagging classifier sınıfı kullanılarak karar ağaçları ile karşılaştırma yapılmıştır.","100 ağacı bir araya getirerek kurulan modelin doğruluk oranı tek bir ağaçta kurulan modelin doğruluk oranından daha yüksek çıkmıştır.","Bagging classifier sınıfı hem de öznitelik örneklemini de destekler, bu max futures ve bootstrap futures hiper parametre ile kontrol edilir."]},"beginTime":397,"endTime":539,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=397&ask_summarization=1"},{"index":6,"title":"Random Forest","list":{"type":"unordered","items":["Random forest karar araçlarının bir assemble'ıdır ve son zamanlarda en yaygın kullanılan makine öğrenmesi algoritmalarından biridir.","Random forest için random forest classifier sınıfı kullanılır ve gerçek dünya uygulamalarında yüz ya da bin gibi daha fazla ağaç kullanılabilir.","Random forest'te onlarca veya yüzlerce ağaç kullanılabilir, bu ağaçlar ön niteliklerin alt kümelerini kullandıkları için daha derine giderler."]},"beginTime":539,"endTime":668,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=539&ask_summarization=1"},{"index":7,"title":"Gradient Boosting","list":{"type":"unordered","items":["Regresyon ağaçları, daha güçlü model oluşturmak için çoklu karar ağaçlarını toplayan diğer bir assemble metottur.","Bu metodun ismi regresyon olmasına rağmen her bir ağaç önceki ağaçların hatalarını doğrulamaya çalışır.","Her bir ağaç verinin bir parçasını iyi tahmin eder ve ağaç eklendikçe modelin performansı artar."]},"beginTime":668,"endTime":697,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=668&ask_summarization=1"},{"index":8,"title":"Modelin Performans Değerlendirmesi","list":{"type":"unordered","items":["Modelin eğitim ve test verisi üzerindeki performansı score metodu kullanılarak değerlendiriliyor.","Modelin eğitim verisindeki doğruluk oranı 1 olarak çıktı, bu muhtemelen aşırı uydurma (overfitting) olduğunu gösteriyor.","Modelin aşırı uydurulması azaltmak için hiper parametreler değiştiriliyor."]},"beginTime":701,"endTime":744,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=701&ask_summarization=1"},{"index":9,"title":"Hiper Parametre Ayarları","list":{"type":"unordered","items":["Hiper parametre, modelde ne kadar çaba harcanacağını gösteriyor.","Learning rate hiper parametresine 0,1 değeri girildiğinde modelin aşırı uydurulması biraz daha azalmış oldu.","Video, assemble konusu hakkında anlatılacakları bu kadardır."]},"beginTime":744,"endTime":774,"href":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=744&ask_summarization=1"}],"linkTemplate":"/video/preview/15696102106614627586?parent-reqid=1765249333732049-7578457588081749369-balancer-l7leveler-kubr-yp-sas-223-BAL&text=Learn+from+Randomness&t=%%timestamp%%&ask_summarization=1"},"isAdultDoc":false,"relatedParams":{"text":"Ensemble learning-random forest & gradient boosting algorithms | machine learning with python 2020","related_orig_text":"Learn from Randomness","related_porno":false,"related_less_3m_off":true,"client":"d2d","no_cnt":1,"related_src":"serp","related":"{\"porno\":false,\"vfp\":1,\"orig_text\":\"Learn from 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is about you. 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