{"id":14427,"date":"2025-03-07T00:15:14","date_gmt":"2025-03-06T21:15:14","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/"},"modified":"2025-03-07T00:15:14","modified_gmt":"2025-03-06T21:15:14","slug":"makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/","title":{"rendered":"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Friedman Testi ve Kritik Fark Diyagramlar\u0131 ile Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131"},"content":{"rendered":"

Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Friedman Testi ve Kritik Fark Diyagramlar\u0131 ile Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/p>\n

Makine \u00f6\u011frenmesinde, farkl\u0131 algoritmalar\u0131n performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak ve en uygun olan\u0131 se\u00e7mek olduk\u00e7a \u00f6nemlidir. Bu kar\u015f\u0131la\u015ft\u0131rmay\u0131 yaparken, tek bir deneyle kesin sonu\u00e7lar elde etmek zor olabilir. \u00c7\u00fcnk\u00fc verilerin yap\u0131s\u0131, algoritmalar\u0131n parametre ayarlar\u0131 gibi fakt\u00f6rler sonu\u00e7lar\u0131 etkiler. Bu nedenle, istatistiksel testler kullanarak algoritmalar\u0131 anlaml\u0131 bir \u015fekilde kar\u015f\u0131la\u015ft\u0131rmak gerekir. Bu makalede, Friedman testi ve Kritik Fark Diyagramlar\u0131 (Critical Difference Diagrams – CDD) kullanarak makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131n performans\u0131n\u0131 nas\u0131l kar\u015f\u0131la\u015ft\u0131rabilece\u011fimizi Python ile g\u00f6sterece\u011fiz.<\/p>\n

Friedman Testi Nedir?<\/h2>\n

Friedman testi, ba\u011f\u0131ml\u0131 \u00f6rnekler (repeated measures) i\u00e7in kullan\u0131lan non-parametrik bir testtir. Birden fazla algoritman\u0131n ayn\u0131 veri k\u00fcmesi \u00fczerindeki performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in uygundur. Test, her algoritman\u0131n performans s\u0131ralamas\u0131n\u0131 kullanarak, algoritmalar aras\u0131nda anlaml\u0131 bir fark olup olmad\u0131\u011f\u0131n\u0131 belirler. Basit\u00e7e s\u00f6ylemek gerekirse, Friedman testi, algoritmalar\u0131n performanslar\u0131nda \u015fans eseri olu\u015fan farkl\u0131l\u0131klardan m\u0131 yoksa ger\u00e7ek bir performans fark\u0131ndan m\u0131 kaynakland\u0131\u011f\u0131n\u0131 anlamam\u0131z\u0131 sa\u011flar. E\u011fer p-de\u011feri belirli bir anlaml\u0131l\u0131k seviyesinden (\u00f6rne\u011fin, 0.05) d\u00fc\u015f\u00fck ise, algoritmalar aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark oldu\u011fu sonucuna varabiliriz.<\/p>\n

Kritik Fark Diyagramlar\u0131 (CDD) Nedir?<\/h2>\n

Friedman testi, algoritmalar aras\u0131nda genel bir fark olup olmad\u0131\u011f\u0131n\u0131 s\u00f6yler. Ancak, hangi algoritmalar\u0131n birbirlerinden istatistiksel olarak farkl\u0131 oldu\u011funu g\u00f6stermez. \u0130\u015fte burada Kritik Fark Diyagramlar\u0131 devreye girer. CDD, Friedman testinin sonucuna dayanarak, algoritmalar\u0131n performans s\u0131ralamalar\u0131n\u0131 ve aralar\u0131ndaki istatistiksel olarak anlaml\u0131 farkl\u0131l\u0131klar\u0131 g\u00f6rselle\u015ftiren bir diyagramd\u0131r. Bu diyagram sayesinde, hangi algoritman\u0131n di\u011ferlerinden daha iyi performans g\u00f6sterdi\u011fini kolayca g\u00f6rebiliriz.<\/p>\n

Python ile Uygulama<\/h2>\n

\u015eimdi, Python kullanarak Friedman testi ve CDD’yi nas\u0131l uygulayabilece\u011fimize bakal\u0131m. \u00d6ncelikle gerekli k\u00fct\u00fcphaneleri i\u00e7e aktarmal\u0131y\u0131z:<\/p>\n

python
\nimport scikit_posthocs as sp
\nimport pandas as pd
\nfrom scipy.stats import friedmanchisquare
\nimport matplotlib.pyplot as plt<\/p>\n

Daha sonra, algoritmalar\u0131n performans sonu\u00e7lar\u0131n\u0131 i\u00e7eren bir DataFrame olu\u015fturmam\u0131z gerekiyor. Bu DataFrame’de, her sat\u0131r bir veri k\u00fcmesini, her s\u00fctun ise bir algoritmay\u0131 temsil eder. H\u00fccreler ise ilgili algoritman\u0131n o veri k\u00fcmesindeki performans skorunu i\u00e7erir. \u00d6rne\u011fin, do\u011fruluk oran\u0131 veya ortalama kare hatas\u0131 olabilir. A\u015fa\u011f\u0131daki kod par\u00e7as\u0131 \u00f6rnek bir DataFrame olu\u015fturur:<\/p>\n

python
\ndata = {‘Algoritma A’: [0.8, 0.7, 0.9, 0.85],
\n ‘Algoritma B’: [0.75, 0.65, 0.8, 0.7],
\n ‘Algoritma C’: [0.9, 0.85, 0.95, 0.92]}
\ndf = pd.DataFrame(data)<\/p>\n

Friedman testini uygulamak i\u00e7in:<\/p>\n

python
\nstatistic, p_value = friedmanchisquare(*df.values.T)
\nprint(f”Friedman Testi \u0130statisti\u011fi: {statistic:.2f}”)
\nprint(f”P-de\u011feri: {p_value:.3f}”)<\/p>\n

E\u011fer p-de\u011feri anlaml\u0131l\u0131k seviyesinden d\u00fc\u015f\u00fckse (\u00f6rne\u011fin, 0.05), algoritmalar aras\u0131nda istatistiksel olarak anlaml\u0131 bir fark vard\u0131r. Bu durumda, CDD olu\u015fturarak hangi algoritmalar\u0131n birbirinden farkl\u0131 oldu\u011funu belirleyebiliriz. Bunun i\u00e7in scikit_posthocs k\u00fct\u00fcphanesini kullanabiliriz:<\/p>\n

python
\nposthoc_results = sp.posthoc_nemenyi_friedman(df)
\nprint(posthoc_results)<\/p>\n

Bu kod, Nemenyi post-hoc testini kullanarak CDD i\u00e7in gerekli verileri \u00fcretir. Sonucu g\u00f6rselle\u015ftirmek i\u00e7in matplotlib k\u00fct\u00fcphanesi kullan\u0131labilir. Daha detayl\u0131 bir g\u00f6rselle\u015ftirme i\u00e7in, buraya<\/a> bakabilirsiniz.<\/p>\n

Sonu\u00e7<\/h2>\n

Friedman testi ve Kritik Fark Diyagramlar\u0131, makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131n performans\u0131n\u0131 istatistiksel olarak kar\u015f\u0131la\u015ft\u0131rmak i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lard\u0131r. Bu y\u00f6ntemler, tek bir deneyin sonu\u00e7lar\u0131na dayanarak kesin yarg\u0131lara varmak yerine, daha g\u00fcvenilir ve anlaml\u0131 sonu\u00e7lar elde etmemizi sa\u011flar. Bu sayede, verilen bir problem i\u00e7in en uygun algoritmay\u0131 se\u00e7me s\u00fcrecinde daha bilin\u00e7li kararlar alabiliriz. Daha fazla bilgi i\u00e7in web sitemi<\/a> ziyaret edebilirsiniz.<\/p>\n

#Etiketler: Friedman Testi, Kritik Fark Diyagramlar\u0131, Makine \u00d6\u011frenmesi, Algoritma Kar\u015f\u0131la\u015ft\u0131rmas\u0131, Python, \u0130statistiksel Testler, Veri Bilimi, CDD, Nemenyi Testi, Performans De\u011ferlendirmesi<\/p>\n","protected":false},"excerpt":{"rendered":"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Friedman Testi ve Kritik Fark Diyagramlar\u0131 ile Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131 Makine \u00f6\u011frenmesinde, farkl\u0131 algoritmalar\u0131n performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak ve…","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-14427","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nMakine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Friedman Testi ve Kritik Fark Diyagramlar\u0131 ile Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131<\/title>\n<meta name=\"description\" content=\"Makine \u00f6\u011frenmesinde, farkl\u0131 algoritmalar\u0131n performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak ve en uygun olan\u0131 se\u00e7mek olduk\u00e7a \u00f6nemlidir. Bu kar\u015f\u0131la\u015ft\u0131rmay\u0131 yaparken, tek bir deneyle kesin sonu\u00e7lar elde etmek zor olabilir. \u00c7\u00fcnk\u00fc verilerin yap\u0131s\u0131, algoritmalar\u0131n parametre ayarlar\u0131 gibi fakt\u00f6rler sonu\u00e7lar\u0131 etkiler. Bu nedenle, istatistiksel testler kullanarak algoritmalar\u0131 anlaml\u0131 bir \u015fekilde kar\u015f\u0131la\u015ft\u0131rmak gerekir. Bu makalede, Friedman testi ve Kritik Fark Diyagramlar\u0131 (Critical Difference Diagrams - CDD) kullanarak makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131n performans\u0131n\u0131 nas\u0131l kar\u015f\u0131la\u015ft\u0131rabilece\u011fimizi Python ile g\u00f6sterece\u011fiz.\" \/>\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\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Friedman Testi ve Kritik Fark Diyagramlar\u0131 ile Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131\" \/>\n<meta property=\"og:description\" content=\"Makine \u00f6\u011frenmesinde, farkl\u0131 algoritmalar\u0131n performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak ve en uygun olan\u0131 se\u00e7mek olduk\u00e7a \u00f6nemlidir. Bu kar\u015f\u0131la\u015ft\u0131rmay\u0131 yaparken, tek bir deneyle kesin sonu\u00e7lar elde etmek zor olabilir. \u00c7\u00fcnk\u00fc verilerin yap\u0131s\u0131, algoritmalar\u0131n parametre ayarlar\u0131 gibi fakt\u00f6rler sonu\u00e7lar\u0131 etkiler. Bu nedenle, istatistiksel testler kullanarak algoritmalar\u0131 anlaml\u0131 bir \u015fekilde kar\u015f\u0131la\u015ft\u0131rmak gerekir. Bu makalede, Friedman testi ve Kritik Fark Diyagramlar\u0131 (Critical Difference Diagrams - CDD) kullanarak makine \u00f6\u011frenmesi algoritmalar\u0131n\u0131n performans\u0131n\u0131 nas\u0131l kar\u015f\u0131la\u015ft\u0131rabilece\u011fimizi Python ile g\u00f6sterece\u011fiz.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2025-03-06T21:15:14+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=\"3 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Friedman Testi ve Kritik Fark Diyagramlar\u0131 ile Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131\",\"datePublished\":\"2025-03-06T21:15:14+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/\"},\"wordCount\":673,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/#respond\"]}],\"copyrightYear\":\"2025\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/makine-ogrenmesi-algoritmalarinin-friedman-testi-ve-kritik-fark-diyagramlari-ile-karsilastirilmasi\/\",\"name\":\"Makine \u00d6\u011frenmesi Algoritmalar\u0131n\u0131n Friedman Testi ve Kritik Fark Diyagramlar\u0131 ile Kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#website\"},\"datePublished\":\"2025-03-06T21:15:14+00:00\",\"description\":\"Makine \u00f6\u011frenmesinde, farkl\u0131 algoritmalar\u0131n performans\u0131n\u0131 kar\u015f\u0131la\u015ft\u0131rmak ve en uygun olan\u0131 se\u00e7mek olduk\u00e7a \u00f6nemlidir. 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