{"id":43799,"date":"2026-08-03T09:00:34","date_gmt":"2026-08-03T06:00:34","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/"},"modified":"2026-08-03T09:00:50","modified_gmt":"2026-08-03T06:00:50","slug":"neden-99-4-dogruluk-yeterli-olmayabilir","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/","title":{"rendered":"Neden 99.4% Do\u011fruluk Yeterli Olmayabilir?"},"content":{"rendered":"<p>Y\u00fcksek do\u011fruluk oran\u0131na sahip ama tamamen i\u015fe yaramaz bir sistemle kar\u015f\u0131la\u015fmak m\u00fcmk\u00fcn m\u00fc? Bu makalede, istatistiksel olarak etkileyici g\u00f6r\u00fcnen ancak ger\u00e7ek d\u00fcnya problemlerini \u00e7\u00f6zmede yetersiz kalan modellerin ard\u0131ndaki nedenleri derinlemesine inceleyece\u011fiz. 99.4% do\u011fruluk, kula\u011fa harika gelse de, do\u011fru ba\u011flamda kullan\u0131lmad\u0131\u011f\u0131nda veya yanl\u0131\u015f metriklerle de\u011ferlendirildi\u011finde yan\u0131lt\u0131c\u0131 olabilir. Bu durumun teknik temellerini, potansiyel tuzaklar\u0131n\u0131 ve bu t\u00fcr senaryolardan nas\u0131l ka\u00e7\u0131n\u0131laca\u011f\u0131n\u0131 ad\u0131m ad\u0131m ele alaca\u011f\u0131z. \u00d6zellikle makine \u00f6\u011frenmesi ve veri bilimi alanlar\u0131nda kar\u015f\u0131m\u0131za \u00e7\u0131kan bu paradoksu anlamak, daha etkili ve anlaml\u0131 \u00e7\u00f6z\u00fcmler geli\u015ftirmemize yard\u0131mc\u0131 olacakt\u0131r.<\/p>\n<h2>Neden 99.4% Do\u011fruluk Yeterli Olmayabilir?<\/h2>\n<p>Bir modelin veya sistemin &#8220;do\u011frulu\u011funu&#8221; \u00f6l\u00e7mek, genellikle ilk bak\u0131\u015fta en sezgisel metrik gibi g\u00f6r\u00fcn\u00fcr. Ancak, \u00f6zellikle dengesiz veri setleri veya belirli s\u0131n\u0131fland\u0131rma g\u00f6revlerinde, y\u00fcksek do\u011fruluk oran\u0131 yan\u0131lt\u0131c\u0131 olabilir. \u00d6rne\u011fin, bir hastal\u0131k te\u015fhis sistemi d\u00fc\u015f\u00fcnelim. E\u011fer pop\u00fclasyonda hastal\u0131\u011f\u0131n g\u00f6r\u00fclme s\u0131kl\u0131\u011f\u0131 \u00e7ok d\u00fc\u015f\u00fckse (%0.1 gibi), sistem t\u00fcm vakalar\u0131 &#8220;hasta de\u011fil&#8221; olarak s\u0131n\u0131fland\u0131rd\u0131\u011f\u0131nda bile %99.9 gibi bir do\u011fruluk oran\u0131na ula\u015fabilir. Bu durumda, ger\u00e7ek hasta vakalar\u0131n\u0131 tespit etme yetene\u011fi s\u0131f\u0131r olurken, do\u011fruluk oran\u0131 son derece y\u00fcksek kal\u0131r. \u0130\u015fte 99.4% gibi bir do\u011fruluk oran\u0131 da benzer bir tuzak bar\u0131nd\u0131rabilir. Bu durum, \u00f6zellikle nadir olaylar\u0131 tespit etmeye \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z senaryolarda kar\u015f\u0131m\u0131za \u00e7\u0131kar. \u00d6rne\u011fin, bir g\u00fcvenlik sisteminde nadir g\u00f6r\u00fclen bir siber sald\u0131r\u0131 t\u00fcr\u00fcn\u00fc tespit etmeye \u00e7al\u0131\u015f\u0131rken, sistemin genel olarak do\u011fru tahminler yapmas\u0131, bu nadir sald\u0131r\u0131lar\u0131 ka\u00e7\u0131rmas\u0131na engel olmayabilir. Bu nedenle, sadece genel do\u011fruluk oran\u0131na odaklanmak yerine, problemin ba\u011flam\u0131na uygun di\u011fer metrikleri de g\u00f6z \u00f6n\u00fcnde bulundurmak b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r.<\/p>\n<h3>Temel Kavramlar: Do\u011fruluk ve \u00d6tesi<\/h3>\n<p>Makine \u00f6\u011frenmesinde bir modelin performans\u0131n\u0131 de\u011ferlendirirken kulland\u0131\u011f\u0131m\u0131z bir\u00e7ok metrik bulunur. Genel do\u011fruluk (accuracy), do\u011fru tahminlerin toplam tahminlere oran\u0131d\u0131r. Ancak bu metrik, veri setindeki s\u0131n\u0131flar\u0131n dengesizli\u011fi kar\u015f\u0131s\u0131nda zay\u0131f kal\u0131r. \u00d6rne\u011fin, 1000 adet veri noktas\u0131ndan 990&#8217;\u0131 &#8220;A&#8221; s\u0131n\u0131f\u0131na, 10&#8217;u ise &#8220;B&#8221; s\u0131n\u0131f\u0131na aitse ve modelimiz t\u00fcm veri noktalar\u0131n\u0131 &#8220;A&#8221; olarak tahmin ederse, do\u011fruluk oran\u0131 %99 olur. Bu, modelin &#8220;B&#8221; s\u0131n\u0131f\u0131n\u0131 hi\u00e7 yakalayamad\u0131\u011f\u0131 anlam\u0131na gelir ki bu da \u00e7o\u011fu durumda kabul edilemez bir sonu\u00e7tur. Bu noktada, daha spesifik metrikler devreye girer:<\/p>\n<ul>\n<li><strong>Kesinlik (Precision):<\/strong> Modelin pozitif olarak tahmin etti\u011fi durumlar\u0131n ne kadar\u0131n\u0131n ger\u00e7ekten pozitif oldu\u011funu g\u00f6sterir. Yanl\u0131\u015f pozitifleri (False Positives) minimize etmek \u00f6nemlidir.<\/li>\n<li><strong>Geri \u00c7a\u011f\u0131rma (Recall) \/ Duyarl\u0131l\u0131k (Sensitivity):<\/strong> Ger\u00e7ek pozitif vakalar\u0131n ne kadar\u0131n\u0131n model taraf\u0131ndan do\u011fru bir \u015fekilde pozitif olarak tahmin edildi\u011fini g\u00f6sterir. Yanl\u0131\u015f negatifleri (False Negatives) minimize etmek \u00f6nemlidir.<\/li>\n<li><strong>F1 Skoru:<\/strong> Kesinlik ve geri \u00e7a\u011f\u0131rman\u0131n harmonik ortalamas\u0131d\u0131r. Dengeli bir performans \u00f6l\u00e7\u00fct\u00fc sunar.<\/li>\n<li><strong>AUC (Area Under the ROC Curve):<\/strong> Farkl\u0131 e\u015fik de\u011ferlerinde modelin performans\u0131n\u0131 g\u00f6steren bir e\u011frinin alt\u0131ndaki aland\u0131r. S\u0131n\u0131fland\u0131rma modellerinin genel ay\u0131rt edici g\u00fcc\u00fcn\u00fc \u00f6l\u00e7er.<\/li>\n<\/ul>\n<p>Bu metrikler, modelin sadece genel ba\u015far\u0131 oran\u0131n\u0131 de\u011fil, ayn\u0131 zamanda belirli hata t\u00fcrlerini ne kadar iyi y\u00f6netti\u011fini de anlamam\u0131z\u0131 sa\u011flar. 99.4% do\u011fruluk oran\u0131, bu metrikler incelendi\u011finde asl\u0131nda \u00e7ok da etkileyici olmayabilir. \u00d6zellikle geri \u00e7a\u011f\u0131rma oran\u0131n\u0131n d\u00fc\u015f\u00fck olmas\u0131, \u00f6nemli vakalar\u0131n g\u00f6zden ka\u00e7\u0131r\u0131lmas\u0131na neden olabilir.<\/p>\n<h2>Vaka Analizi 1: T\u0131bbi Te\u015fhis Sistemi Paradoksu<\/h2>\n<p>Diyelim ki, nadir g\u00f6r\u00fclen bir kanser t\u00fcr\u00fcn\u00fc tespit etmek i\u00e7in geli\u015ftirilmi\u015f bir makine \u00f6\u011frenmesi modelimiz var. Bu kanser t\u00fcr\u00fc, genel n\u00fcfusun yaln\u0131zca %0.5&#8217;inde g\u00f6r\u00fcl\u00fcyor. Modelimiz, 100.000 ki\u015fiyi tar\u0131yor ve 500 ki\u015fide bu kanseri tespit ediyor. E\u011fer modelimiz, bu 500 ki\u015finin tamam\u0131n\u0131 do\u011fru bir \u015fekilde &#8220;kanserli&#8221; olarak i\u015faretlerse ve geri kalan 99.500 ki\u015fiyi de do\u011fru bir \u015fekilde &#8220;kanserli de\u011fil&#8221; olarak i\u015faretlerse, modelimizin do\u011fruluk oran\u0131 %100 olur. Ancak, pratikte bu kadar m\u00fckemmel bir model bulmak neredeyse imkans\u0131zd\u0131r. Ger\u00e7ek\u00e7i bir senaryoda, modelimiz %99.4 do\u011fruluk oran\u0131na ula\u015fabilir. Bu ne anlama gelir? Bu, modelin genel olarak do\u011fru tahminler yapt\u0131\u011f\u0131n\u0131 g\u00f6sterir. Ancak, e\u011fer modelimiz t\u00fcm 100.000 ki\u015fiyi &#8220;kanserli de\u011fil&#8221; olarak s\u0131n\u0131fland\u0131r\u0131rsa bile, do\u011fruluk oran\u0131 %99.5 olurdu. E\u011fer modelimiz, asl\u0131nda kanserli olmayan 100 ki\u015fiyi yanl\u0131\u015fl\u0131kla kanserli olarak i\u015faretlerse (yanl\u0131\u015f pozitif) ve kanserli olan 400 ki\u015fiyi do\u011fru tespit ederse (do\u011fru pozitif), toplam tahmin say\u0131s\u0131 100.000&#8217;dir. Do\u011fru tahmin say\u0131s\u0131 400 (do\u011fru pozitif) + 99.500 (ger\u00e7ekte kanserli olmayan ve yanl\u0131\u015fl\u0131kla kanserli olmayan olarak tahmin edilenler) = 99.900 olur. Bu durumda do\u011fruluk oran\u0131 99.900 \/ 100.000 = %99.9 olur. Fakat bu senaryoda, modelin ger\u00e7ek kanser vakalar\u0131n\u0131 tespit etme kabiliyeti, yani geri \u00e7a\u011f\u0131rma (recall) oran\u0131, 400 \/ 500 = %80 olurdu. Peki ya modelimiz, sadece 100 ki\u015fiyi kanserli olarak i\u015faretlerse ve bunlar\u0131n 50&#8217;si ger\u00e7ekten kanserliyse (do\u011fru pozitif) ve 50&#8217;si kanserli de\u011filse (yanl\u0131\u015f pozitif)? Geriye kalan 99.900 ki\u015fiyi de kanserli de\u011fil olarak i\u015faretlerse ve bunlar\u0131n 450&#8217;si kanserliyse (yanl\u0131\u015f negatif) ve 99.450&#8217;si kanserli de\u011filse (do\u011fru negatif). Bu durumda toplam do\u011fru tahmin say\u0131s\u0131 50 (do\u011fru pozitif) + 99.450 (do\u011fru negatif) = 99.500 olur. Do\u011fruluk oran\u0131 99.500 \/ 100.000 = %99.5 olur. \u0130\u015fte bu senaryoda, yanl\u0131\u015f negatif say\u0131s\u0131 450 olurdu. E\u011fer modelimiz %99.4 do\u011fruluk oran\u0131na ula\u015f\u0131rsa, bu 100.000 ki\u015fiden 994 tanesini do\u011fru tahmin etmi\u015f demektir. Kalan 600 ki\u015fi ise yanl\u0131\u015f tahmin edilmi\u015ftir. E\u011fer bu 600 yanl\u0131\u015f tahminin b\u00fcy\u00fck \u00e7o\u011funlu\u011fu kanserli olan vakalarsa (yani yanl\u0131\u015f negatifler), o zaman modelimiz y\u00fcksek do\u011fruluk oran\u0131na ra\u011fmen i\u015fe yaramaz hale gelir. Bu durumda, 500 kanserli vakadan sadece 440&#8217;\u0131n\u0131 tespit etmi\u015f oluruz (do\u011fru pozitif), bu da %88&#8217;lik bir geri \u00e7a\u011f\u0131rma oran\u0131na denk gelir. Kalan 60 kanserli vakay\u0131 ise ka\u00e7\u0131rm\u0131\u015f oluruz. Bu, hayat kurtaran bir te\u015fhis sisteminde kabul edilemez bir durumdur.<\/p>\n<p>Bu \u00f6rnekte g\u00f6r\u00fcld\u00fc\u011f\u00fc gibi, %99.4 do\u011fruluk oran\u0131, asl\u0131nda 60 adet kanserli vakay\u0131 ka\u00e7\u0131rd\u0131\u011f\u0131m\u0131z anlam\u0131na gelebilir. Bu da, hastal\u0131\u011f\u0131n erken te\u015fhis edilmemesi ve tedavinin gecikmesi gibi ciddi sonu\u00e7lara yol a\u00e7abilir. Bu nedenle, t\u0131bbi te\u015fhis gibi kritik alanlarda sadece genel do\u011fruluk oran\u0131na bakmak yerine, yanl\u0131\u015f negatif oran\u0131n\u0131n ne kadar d\u00fc\u015f\u00fck tutuldu\u011fu gibi metrikler \u00e7ok daha \u00f6nemlidir. Geri \u00e7a\u011f\u0131rma (recall) oran\u0131n\u0131n y\u00fcksek olmas\u0131, yani ger\u00e7ek pozitif vakalar\u0131n b\u00fcy\u00fck \u00e7o\u011funlu\u011funun tespit edilmesi, bu t\u00fcr sistemlerde \u00f6ncelikli hedef olmal\u0131d\u0131r.<\/p>\n<h2>Neden Veri Dengesizli\u011fi Bir Sorun Haline Geliyor?<\/h2>\n<p>Veri dengesizli\u011fi, makine \u00f6\u011frenmesi modellerinin performans\u0131n\u0131 olumsuz etkileyen en yayg\u0131n sorunlardan biridir. Bir veri setindeki s\u0131n\u0131flar aras\u0131nda b\u00fcy\u00fck farkl\u0131l\u0131klar oldu\u011funda, model \u00e7o\u011funluk s\u0131n\u0131f\u0131na do\u011fru e\u011filim g\u00f6sterir. Bu durum, az\u0131nl\u0131k s\u0131n\u0131f\u0131ndaki \u00f6rnekleri do\u011fru bir \u015fekilde \u00f6\u011frenmesini zorla\u015ft\u0131r\u0131r. \u00d6rne\u011fin, bankac\u0131l\u0131k doland\u0131r\u0131c\u0131l\u0131k tespiti senaryosunda, doland\u0131r\u0131c\u0131l\u0131k i\u015flemleri genellikle toplam i\u015flem say\u0131s\u0131n\u0131n \u00e7ok k\u00fc\u00e7\u00fck bir k\u0131sm\u0131n\u0131 olu\u015fturur. E\u011fer bir model, t\u00fcm i\u015flemleri &#8220;doland\u0131r\u0131c\u0131l\u0131k de\u011fil&#8221; olarak s\u0131n\u0131fland\u0131r\u0131rsa, do\u011fruluk oran\u0131 \u00e7ok y\u00fcksek olabilir. Ancak, ger\u00e7ek doland\u0131r\u0131c\u0131l\u0131klar\u0131 tespit etme yetene\u011fi s\u0131f\u0131r olacakt\u0131r. Bu, finansal kay\u0131plara yol a\u00e7abilir. Bu dengesizli\u011fi gidermek i\u00e7in \u00e7e\u015fitli teknikler kullan\u0131l\u0131r:<\/p>\n<ul>\n<li><strong>A\u015f\u0131r\u0131 \u00d6rnekleme (Oversampling):<\/strong> Az\u0131nl\u0131k s\u0131n\u0131f\u0131ndaki \u00f6rneklerin say\u0131s\u0131n\u0131 art\u0131rmak. Bu, \u00e7o\u011funluk s\u0131n\u0131f\u0131ndaki \u00f6rneklerin kopyalanmas\u0131 veya sentetik veri \u00fcretimi yoluyla yap\u0131labilir.<\/li>\n<li><strong>Az \u00d6rnekleme (Undersampling):<\/strong> \u00c7o\u011funluk s\u0131n\u0131f\u0131ndaki \u00f6rneklerin say\u0131s\u0131n\u0131 azaltmak. Bu, rastgele \u00f6rneklerin silinmesi veya daha ak\u0131ll\u0131 y\u00f6ntemlerle yap\u0131labilir.<\/li>\n<li><strong>Sentetik Veri \u00dcretimi (SMOTE &#8211; Synthetic Minority Over-sampling Technique):<\/strong> Az\u0131nl\u0131k s\u0131n\u0131f\u0131ndaki \u00f6rnekler aras\u0131ndaki bo\u015fluklar\u0131 doldurarak yeni sentetik \u00f6rnekler olu\u015fturmak.<\/li>\n<li><strong>Model Tabanl\u0131 Yakla\u015f\u0131mlar:<\/strong> Maliyet duyarl\u0131 \u00f6\u011frenme (cost-sensitive learning) gibi teknikler, yanl\u0131\u015f s\u0131n\u0131fland\u0131rmalar\u0131n maliyetini farkl\u0131 s\u0131n\u0131flar i\u00e7in ayarlayarak modelin az\u0131nl\u0131k s\u0131n\u0131f\u0131na daha fazla odaklanmas\u0131n\u0131 sa\u011flar.<\/li>\n<\/ul>\n<p>Bu teknikler, veri setini daha dengeli hale getirerek modelin az\u0131nl\u0131k s\u0131n\u0131f\u0131n\u0131 daha iyi \u00f6\u011frenmesini sa\u011flar. Aksi takdirde, %99.4 do\u011fruluk oran\u0131, asl\u0131nda \u00e7o\u011funluk s\u0131n\u0131f\u0131n\u0131 do\u011fru tahmin etmekten ibaret olabilir ve az\u0131nl\u0131k s\u0131n\u0131f\u0131ndaki \u00f6nemli olaylar\u0131 g\u00f6zden ka\u00e7\u0131rabilir.<\/p>\n<h2>Vaka Analizi 2: Sahtecilik Tespiti Sisteminin Tuzaklar\u0131<\/h2>\n<p>Bir kredi kart\u0131 \u015firketi, milyonlarca i\u015flemi i\u015flerken doland\u0131r\u0131c\u0131l\u0131klar\u0131 tespit etmek i\u00e7in bir sistem geli\u015ftiriyor. \u0130\u015flemlerin sadece %0.1&#8217;i doland\u0131r\u0131c\u0131l\u0131k olarak ger\u00e7ekle\u015fiyor. Geli\u015ftirilen model, t\u00fcm i\u015flemlerin %99.9&#8217;unu do\u011fru bir \u015fekilde &#8220;doland\u0131r\u0131c\u0131l\u0131k de\u011fil&#8221; olarak s\u0131n\u0131fland\u0131rarak %99.9 do\u011fruluk oran\u0131na ula\u015f\u0131yor. Bu kula\u011fa harika gelse de, ger\u00e7ekte durum farkl\u0131d\u0131r. E\u011fer modelimiz, 10 milyon i\u015flemden sadece 10.000 tanesinin doland\u0131r\u0131c\u0131l\u0131k oldu\u011funu biliyorsak ve t\u00fcm i\u015flemleri &#8220;doland\u0131r\u0131c\u0131l\u0131k de\u011fil&#8221; olarak s\u0131n\u0131fland\u0131r\u0131rsa, do\u011fruluk oran\u0131 yine %99.9 olacakt\u0131r. Ancak, bu durumda hi\u00e7bir doland\u0131r\u0131c\u0131l\u0131k vakas\u0131 tespit edilemez ve \u015firket b\u00fcy\u00fck zarara u\u011frar. Senaryoyu biraz daha ger\u00e7ek\u00e7i hale getirelim: Modelimiz, 10 milyon i\u015flemde %99.4 do\u011fruluk oran\u0131na ula\u015f\u0131yor. Bu, 60.000 i\u015flemin yanl\u0131\u015f tahmin edildi\u011fi anlam\u0131na geliyor. E\u011fer bu yanl\u0131\u015f tahminlerin 9.000 tanesi ger\u00e7ek doland\u0131r\u0131c\u0131l\u0131k vakas\u0131ysa (yani modelimiz 10.000 doland\u0131r\u0131c\u0131l\u0131ktan sadece 1.000&#8217;ini tespit edebilmi\u015fse), o zaman geri \u00e7a\u011f\u0131rma oran\u0131 %10 olur. Bu, \u015firketin doland\u0131r\u0131c\u0131l\u0131klar\u0131 tespit etme yetene\u011finin son derece zay\u0131f oldu\u011funu g\u00f6sterir. Di\u011fer yandan, e\u011fer bu 60.000 yanl\u0131\u015f tahminin b\u00fcy\u00fck \u00e7o\u011funlu\u011fu, asl\u0131nda doland\u0131r\u0131c\u0131l\u0131k olmayan ama model taraf\u0131ndan yanl\u0131\u015fl\u0131kla doland\u0131r\u0131c\u0131l\u0131k olarak i\u015faretlenen i\u015flemlerse (yanl\u0131\u015f pozitif), o zaman bu durum da m\u00fc\u015fteri memnuniyetini d\u00fc\u015f\u00fcrebilir ve operasyonel maliyetleri art\u0131rabilir. Bu nedenle, doland\u0131r\u0131c\u0131l\u0131k tespiti gibi alanlarda kesinlik (precision) ve geri \u00e7a\u011f\u0131rma (recall) metrikleri, genel do\u011fruluk oran\u0131ndan \u00e7ok daha \u00f6nemlidir. Y\u00fcksek bir geri \u00e7a\u011f\u0131rma oran\u0131, yani m\u00fcmk\u00fcn oldu\u011funca \u00e7ok doland\u0131r\u0131c\u0131l\u0131k vakas\u0131n\u0131n tespit edilmesi, \u00f6ncelikli hedef olmal\u0131d\u0131r. Bu, bazen daha fazla yanl\u0131\u015f pozitifin kabul edilmesi anlam\u0131na gelse bile, mali kay\u0131plar\u0131 \u00f6nlemek ad\u0131na daha iyi bir strateji olabilir.<\/p>\n<h2>Do\u011fru Metri\u011fi Se\u00e7mek: Problemin Ba\u011flam\u0131 Neden \u00d6nemli?<\/h2>\n<p>Bir modelin ba\u015far\u0131s\u0131n\u0131 de\u011ferlendirirken do\u011fru metrikleri se\u00e7mek, problemin do\u011fas\u0131na ve i\u015f hedeflerine ba\u011fl\u0131d\u0131r. \u00d6rne\u011fin, spam e-posta filtresi geli\u015ftiriyorsak, \u00f6nemli olan, \u00f6nemli e-postalar\u0131n spam olarak i\u015faretlenmemesidir (d\u00fc\u015f\u00fck yanl\u0131\u015f pozitif). Bu durumda kesinlik (precision) daha \u00f6n planda olabilir. Ancak, bir t\u0131bbi te\u015fhis sisteminde, kanserli bir hastan\u0131n g\u00f6zden ka\u00e7\u0131r\u0131lmamas\u0131 (d\u00fc\u015f\u00fck yanl\u0131\u015f negatif) daha kritiktir. Bu durumda geri \u00e7a\u011f\u0131rma (recall) daha \u00f6nemlidir. 99.4% do\u011fruluk oran\u0131, hangi metriklerin g\u00f6z ard\u0131 edildi\u011fini veya yeterince iyi olmad\u0131\u011f\u0131n\u0131 bize s\u00f6ylemez. Bu nedenle, bir modelin performans\u0131n\u0131 de\u011ferlendirirken a\u015fa\u011f\u0131daki sorular\u0131 kendimize sormal\u0131y\u0131z:<\/p>\n<ul>\n<li>Bu modelin yanl\u0131\u015f tahminlerinin maliyeti nedir?<\/li>\n<li>Hangi t\u00fcr hatalar (yanl\u0131\u015f pozitif veya yanl\u0131\u015f negatif) daha kabul edilemez?<\/li>\n<li>Modelin hedefledi\u011fi i\u015f problemi nedir ve bu problemi \u00e7\u00f6zmek i\u00e7in hangi performans g\u00f6stergeleri en uygunudur?<\/li>\n<\/ul>\n<p>Bu sorular\u0131n cevaplar\u0131, hangi metriklerin daha fazla odaklan\u0131lmas\u0131 gerekti\u011fini belirleyecektir. Sadece y\u00fcksek bir do\u011fruluk oran\u0131, problemin tam olarak \u00e7\u00f6z\u00fcld\u00fc\u011f\u00fc anlam\u0131na gelmez. \u00d6rne\u011fin, bir \u00f6neri sisteminde, kullan\u0131c\u0131lar\u0131n %99.4&#8217;\u00fcne rastgele \u00fcr\u00fcnler \u00f6nermek ve sadece %0.6&#8217;s\u0131na ger\u00e7ekten ilgi duyacaklar\u0131 \u00fcr\u00fcnleri \u00f6nermek, %99.4 do\u011fruluk oran\u0131 (e\u011fer do\u011fruluk, do\u011fru bir \u015fekilde ilgisiz \u00fcr\u00fcn \u00f6nerme olarak tan\u0131mlan\u0131rsa) elde etmemizi sa\u011flayabilir. Ancak bu, sistemin as\u0131l amac\u0131n\u0131 yerine getirmedi\u011fi anlam\u0131na gelir. Kullan\u0131c\u0131lar\u0131n ilgisini \u00e7ekecek \u00fcr\u00fcnleri do\u011fru bir \u015fekilde \u00f6nermesi, yani y\u00fcksek bir hassasiyet ve geri \u00e7a\u011f\u0131rma ile \u00e7al\u0131\u015fmas\u0131 gerekir.<\/p>\n<h2>Uygulamal\u0131 K\u0131s\u0131m: Python ile Metrikleri Hesaplama<\/h2>\n<p>Python&#8217;da <code>scikit-learn<\/code> k\u00fct\u00fcphanesi, model performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in \u00e7e\u015fitli metrikler sunar. A\u015fa\u011f\u0131da, \u00f6rnek bir veri seti \u00fczerinden bu metriklerin nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6steren bir kod \u00f6rne\u011fi bulunmaktad\u0131r. Bu \u00f6rnekte, dengesiz bir veri seti ve bir s\u0131n\u0131fland\u0131rma modeli kullanaca\u011f\u0131z.<\/p>\n<p>\u00d6ncelikle gerekli k\u00fct\u00fcphaneleri i\u00e7e aktaral\u0131m:<\/p>\n<pre><code class=\"language-html\">&lt;div class=&quot;code-container&quot;&gt;\n      &lt;pre&gt;&lt;code&gt;\n    from sklearn.model_selection import train_test_split\n    from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n    from sklearn.linear_model import LogisticRegression\n    from sklearn.datasets import make_classification\n    import pandas as pd\n      &lt;\/code&gt;&lt;\/pre&gt;\n    &lt;\/div&gt;<\/code><\/pre>\n<p>\u015eimdi dengesiz bir veri seti olu\u015ftural\u0131m. Burada <code>n_samples<\/code> toplam \u00f6rnek say\u0131s\u0131n\u0131, <code>weights<\/code> ise s\u0131n\u0131flar\u0131n oran\u0131n\u0131 belirtir. <code>weights=[0.95, 0.05]<\/code> ile %95&#8217;i bir s\u0131n\u0131fa, %5&#8217;i di\u011fer s\u0131n\u0131fa ait bir veri seti olu\u015fturuyoruz.<\/p>\n<pre><code class=\"language-html\">&lt;div class=&quot;code-container&quot;&gt;\n      &lt;pre&gt;&lt;code&gt;\n    X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=5,\n                               n_classes=2, weights=[0.95, 0.05], flip_y=0.01, random_state=42)\n\n    # Veriyi e\u011fitim ve test setlerine ay\u0131rma\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42, stratify=y)\n      &lt;\/code&gt;&lt;\/pre&gt;\n    &lt;\/div&gt;<\/code><\/pre>\n<p>Bir lojistik regresyon modeli e\u011fitelim ve test seti \u00fczerinde tahminler yapal\u0131m.<\/p>\n<pre><code class=\"language-html\">&lt;div class=&quot;code-container&quot;&gt;\n      &lt;pre&gt;&lt;code&gt;\n    model = LogisticRegression(solver=&#039;liblinear&#039;, random_state=42)\n    model.fit(X_train, y_train)\n    y_pred = model.predict(X_test)\n    y_pred_proba = model.predict_proba(X_test)[:, 1] # Pozitif s\u0131n\u0131f olas\u0131l\u0131klar\u0131\n      &lt;\/code&gt;&lt;\/pre&gt;\n    &lt;\/div&gt;<\/code><\/pre>\n<p>\u015eimdi hesaplad\u0131\u011f\u0131m\u0131z metrikleri bir DataFrame i\u00e7inde g\u00f6sterelim.<\/p>\n<pre><code class=\"language-html\">&lt;div class=&quot;code-container&quot;&gt;\n      &lt;pre&gt;&lt;code&gt;\n    accuracy = accuracy_score(y_test, y_pred)\n    precision = precision_score(y_test, y_pred)\n    recall = recall_score(y_test, y_pred)\n    f1 = f1_score(y_test, y_pred)\n    roc_auc = roc_auc_score(y_test, y_pred_proba)\n\n    metrics_df = pd.DataFrame({\n        &#039;Metric&#039;: [&#039;Accuracy&#039;, &#039;Precision&#039;, &#039;Recall&#039;, &#039;F1 Score&#039;, &#039;ROC AUC&#039;],\n        &#039;Score&#039;: [accuracy, precision, recall, f1, roc_auc]\n    })\n\n    print(metrics_df)\n      &lt;\/code&gt;&lt;\/pre&gt;\n    &lt;\/div&gt;<\/code><\/pre>\n<p>Bu kod \u00e7\u0131kt\u0131s\u0131, modelimizin farkl\u0131 metriklerdeki performans\u0131n\u0131 a\u00e7\u0131k\u00e7a g\u00f6sterecektir. E\u011fer do\u011fruluk oran\u0131 y\u00fcksek iken geri \u00e7a\u011f\u0131rma oran\u0131 d\u00fc\u015f\u00fckse, bu, modelin az\u0131nl\u0131k s\u0131n\u0131f\u0131n\u0131 (bu \u00f6rnekte genellikle doland\u0131r\u0131c\u0131l\u0131k veya nadir hastal\u0131k gibi) yeterince iyi tespit edemedi\u011fini g\u00f6sterir. Bu durumda, %99.4 gibi bir do\u011fruluk oran\u0131 yan\u0131lt\u0131c\u0131 olabilir.<\/p>\n<h2>\u0130leri D\u00fczey: Modelin Performans\u0131n\u0131 Art\u0131rma Yollar\u0131<\/h2>\n<p>E\u011fer bir modelin %99.4 do\u011fruluk oran\u0131na sahip olmas\u0131na ra\u011fmen i\u015fe yaramaz oldu\u011funu fark edersek, bu durumu iyile\u015ftirmek i\u00e7in atabilece\u011fimiz ad\u0131mlar vard\u0131r. \u00d6ncelikle, problemin do\u011fru bir \u015fekilde tan\u0131mland\u0131\u011f\u0131ndan ve uygun metriklerin se\u00e7ildi\u011finden emin olmal\u0131y\u0131z. Ard\u0131ndan, a\u015fa\u011f\u0131daki stratejileri uygulayabiliriz:<\/p>\n<ul>\n<li><strong>Veri Art\u0131rma ve \u00d6n \u0130\u015fleme:<\/strong> Daha fazla veri toplamak, mevcut verileri temizlemek ve \u00f6zellik m\u00fchendisli\u011fi (feature engineering) ile daha anlaml\u0131 \u00f6zellikler \u00e7\u0131karmak performans art\u0131\u015f\u0131na katk\u0131 sa\u011flayabilir.<\/li>\n<li><strong>Dengesiz Veri Seti Teknikleri:<\/strong> Yukar\u0131da bahsedilen a\u015f\u0131r\u0131 \u00f6rnekleme, az \u00f6rnekleme veya SMOTE gibi teknikler, modelin az\u0131nl\u0131k s\u0131n\u0131flar\u0131n\u0131 daha iyi \u00f6\u011frenmesine yard\u0131mc\u0131 olabilir.<\/li>\n<li><strong>Farkl\u0131 Algoritmalar Denemek:<\/strong> Lojistik regresyon yerine, karar a\u011fa\u00e7lar\u0131 (decision trees), rastgele ormanlar (random forests), gradient boosting makineleri (GBM) veya sinir a\u011flar\u0131 (neural networks) gibi daha karma\u015f\u0131k algoritmalar, \u00f6zellikle karma\u015f\u0131k ili\u015fkileri yakalamada daha ba\u015far\u0131l\u0131 olabilir.<\/li>\n<li><strong>Hiperparametre Optimizasyonu:<\/strong> Modelin hiperparametrelerini ayarlamak (\u00f6rne\u011fin, \u00f6\u011frenme oran\u0131, a\u011fa\u00e7 derinli\u011fi gibi), performans \u00fczerinde \u00f6nemli bir etkiye sahip olabilir. Grid Search veya Random Search gibi y\u00f6ntemler kullan\u0131labilir.<\/li>\n<li><strong>Model Topluluklar\u0131 (Ensemble Methods):<\/strong> Birden fazla modeli bir araya getirerek daha g\u00fc\u00e7l\u00fc bir model olu\u015fturmak. \u00d6rne\u011fin, rastgele ormanlar, birden fazla karar a\u011fac\u0131n\u0131 birle\u015ftirir.<\/li>\n<li><strong>Maliyet Duyarl\u0131 \u00d6\u011frenme (Cost-Sensitive Learning):<\/strong> Yanl\u0131\u015f s\u0131n\u0131fland\u0131rmalar\u0131n maliyetini farkl\u0131 s\u0131n\u0131flar i\u00e7in farkl\u0131 ayarlayarak, modelin daha kritik s\u0131n\u0131flara daha fazla odaklanmas\u0131n\u0131 sa\u011flamak.<\/li>\n<\/ul>\n<p>Bu ad\u0131mlar, modelin sadece genel do\u011fruluk oran\u0131n\u0131 de\u011fil, ayn\u0131 zamanda problemin \u00f6z\u00fcn\u00fc olu\u015fturan kritik g\u00f6revleri de yerine getirmesini sa\u011flayacakt\u0131r. \u00d6rne\u011fin, bir finansal doland\u0131r\u0131c\u0131l\u0131k tespit sisteminde, bir yanl\u0131\u015f negatifin maliyeti, bir yanl\u0131\u015f pozitifin maliyetinden \u00e7ok daha y\u00fcksek olabilir. Maliyet duyarl\u0131 \u00f6\u011frenme ile bu durumu modelimize yans\u0131tabiliriz.<\/p>\n<h2>Sonu\u00e7: Do\u011fruluk Tek Ba\u015f\u0131na Yeterli De\u011fil<\/h2>\n<p>Sonu\u00e7 olarak, bir modelin veya sistemin %99.4 gibi y\u00fcksek bir do\u011fruluk oran\u0131na sahip olmas\u0131, tek ba\u015f\u0131na onun ba\u015far\u0131l\u0131 veya i\u015fe yarar oldu\u011fu anlam\u0131na gelmez. Bu y\u00fcksek do\u011fruluk oran\u0131, \u00f6zellikle dengesiz veri setlerinde veya yanl\u0131\u015f metriklerle de\u011ferlendirildi\u011finde yan\u0131lt\u0131c\u0131 olabilir. Problemin ba\u011flam\u0131n\u0131 anlamak, do\u011fru performans metriklerini se\u00e7mek ve bu metrikler \u00fczerinden modelin performans\u0131n\u0131 de\u011ferlendirmek b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r. T\u0131bbi te\u015fhis, finansal doland\u0131r\u0131c\u0131l\u0131k tespiti, siber g\u00fcvenlik gibi alanlarda, yanl\u0131\u015f negatiflerin veya yanl\u0131\u015f pozitiflerin sonu\u00e7lar\u0131 \u00e7ok ciddi olabilir. Bu nedenle, sadece genel do\u011fruluk oran\u0131na odaklanmak yerine, kesinlik, geri \u00e7a\u011f\u0131rma, F1 skoru ve AUC gibi metrikleri de dikkate alarak daha kapsaml\u0131 bir de\u011ferlendirme yapmak gereklidir. Y\u00fcksek do\u011fruluk oran\u0131na ra\u011fmen i\u015fe yaramayan sistemler, genellikle veri dengesizli\u011fi, yanl\u0131\u015f metrik se\u00e7imi veya problemin yanl\u0131\u015f anla\u015f\u0131lmas\u0131ndan kaynaklan\u0131r. Bu tuzaklardan ka\u00e7\u0131nmak ve ger\u00e7ek d\u00fcnya problemlerini etkili bir \u015fekilde \u00e7\u00f6zebilen modeller geli\u015ftirmek i\u00e7in dikkatli bir analiz ve do\u011fru de\u011ferlendirme y\u00f6ntemleri \u015fartt\u0131r.<\/p>\n<h3>S\u0131k\u00e7a Sorulan Sorular<\/h3>\n<ul>\n<li>\n            <strong>Soru:<\/strong> Bir modelin do\u011fruluk oran\u0131 %99.4 iken neden i\u015fe yaramaz olabilir?<br \/>\n            <strong>Cevap:<\/strong> Bu durum genellikle dengesiz veri setlerinden kaynaklan\u0131r. E\u011fer veri setindeki s\u0131n\u0131flardan biri di\u011ferinden \u00e7ok daha fazlaysa, model \u00e7o\u011funluk s\u0131n\u0131f\u0131n\u0131 do\u011fru tahmin ederek y\u00fcksek do\u011fruluk oran\u0131na ula\u015fabilir, ancak az\u0131nl\u0131k s\u0131n\u0131f\u0131ndaki \u00f6nemli olaylar\u0131 ka\u00e7\u0131rabilir. \u00d6rne\u011fin, nadir bir hastal\u0131\u011f\u0131 tespit eden bir sistemde, hastal\u0131\u011f\u0131n g\u00f6r\u00fclme oran\u0131 \u00e7ok d\u00fc\u015f\u00fckse, sistem t\u00fcm vakalar\u0131 &#8220;sa\u011fl\u0131kl\u0131&#8221; olarak i\u015faretlese bile y\u00fcksek do\u011fruluk oran\u0131na ula\u015fabilir.\n        <\/li>\n<li>\n            <strong>Soru:<\/strong> Dengesiz veri setleriyle ba\u015fa \u00e7\u0131kmak i\u00e7in hangi y\u00f6ntemler kullan\u0131l\u0131r?<br \/>\n            <strong>Cevap:<\/strong> Dengesiz veri setleriyle ba\u015fa \u00e7\u0131kmak i\u00e7in a\u015f\u0131r\u0131 \u00f6rnekleme (oversampling), az \u00f6rnekleme (undersampling), sentetik veri \u00fcretimi (SMOTE) ve maliyet duyarl\u0131 \u00f6\u011frenme (cost-sensitive learning) gibi teknikler kullan\u0131l\u0131r. Bu y\u00f6ntemler, az\u0131nl\u0131k s\u0131n\u0131f\u0131n\u0131n model taraf\u0131ndan daha iyi \u00f6\u011frenilmesini sa\u011flamay\u0131 hedefler.\n        <\/li>\n<li>\n            <strong>Soru:<\/strong> Do\u011fruluk oran\u0131 d\u0131\u015f\u0131nda hangi metrikler \u00f6nemlidir?<br \/>\n            <strong>Cevap:<\/strong> Problemin ba\u011flam\u0131na ba\u011fl\u0131 olarak kesinlik (precision), geri \u00e7a\u011f\u0131rma (recall), F1 skoru ve AUC (Area Under the ROC Curve) gibi metrikler de \u00e7ok \u00f6nemlidir. T\u0131bbi te\u015fhis gibi alanlarda geri \u00e7a\u011f\u0131rma, finansal doland\u0131r\u0131c\u0131l\u0131k tespitinde ise hem kesinlik hem de geri \u00e7a\u011f\u0131rma de\u011ferleri kritik \u00f6nem ta\u015f\u0131r.\n        <\/li>\n<li>\n            <strong>Soru:<\/strong> Bir modelin performans\u0131n\u0131 de\u011ferlendirirken en iyi yakla\u015f\u0131m nedir?<br \/>\n            <strong>Cevap:<\/strong> En iyi yakla\u015f\u0131m, problemin amac\u0131n\u0131 ve yanl\u0131\u015f tahminlerin potansiyel sonu\u00e7lar\u0131n\u0131 anlamakt\u0131r. Tek bir metri\u011fe odaklanmak yerine, problemin do\u011fas\u0131na en uygun birka\u00e7 metri\u011fi birlikte de\u011ferlendirmek ve modelin bu metriklerdeki performans\u0131n\u0131 analiz etmek daha do\u011fru bir de\u011ferlendirme sa\u011flar.\n        <\/li>\n<\/ul>\n<p>#Makine\u00d6\u011frenmesi #VeriBilimi #YapayZeka #ModelDe\u011ferlendirme #Do\u011frulukOran\u0131<\/p>\n<div class=\"github-example-link\"><strong>\u00d6rnek kod:<\/strong> <a href=\"https:\/\/github.com\/fatihsoysalcom\/misleading-accuracy-in-imbalanced-datasets\" target=\"_blank\" rel=\"noopener noreferrer\">github.com\/fatihsoysalcom\/misleading-accuracy-in-imbalanced-datasets<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"Y\u00fcksek do\u011fruluk oran\u0131na sahip ama tamamen i\u015fe yaramaz bir sistemle kar\u015f\u0131la\u015fmak m\u00fcmk\u00fcn m\u00fc?","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-43799","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>Neden 99.4% Do\u011fruluk Yeterli Olmayabilir? - Kodlar\u0131n Gizemli D\u00fcnyas\u0131<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Neden 99.4% Do\u011fruluk Yeterli Olmayabilir?\" \/>\n<meta property=\"og:description\" content=\"Y\u00fcksek do\u011fruluk oran\u0131na sahip ama tamamen i\u015fe yaramaz bir sistemle kar\u015f\u0131la\u015fmak m\u00fcmk\u00fcn m\u00fc?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/\" \/>\n<meta property=\"og:site_name\" content=\"Kodlar\u0131n Gizemli D\u00fcnyas\u0131\" \/>\n<meta property=\"article:published_time\" content=\"2026-08-03T06:00:34+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-08-03T06:00:50+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=\"17 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/\"},\"author\":{\"name\":\"Fatih Soysal\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"headline\":\"Neden 99.4% Do\u011fruluk Yeterli Olmayabilir?\",\"datePublished\":\"2026-08-03T06:00:34+00:00\",\"dateModified\":\"2026-08-03T06:00:50+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/\"},\"wordCount\":3192,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#\/schema\/person\/002a254750921dcfd568a99e48240dd1\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/#respond\"]}],\"copyrightYear\":\"2026\",\"copyrightHolder\":{\"@id\":\"https:\/\/fatihsoysal.com\/blog\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/\",\"url\":\"https:\/\/fatihsoysal.com\/blog\/neden-99-4-dogruluk-yeterli-olmayabilir\/\",\"name\":\"Neden 99.4% Do\u011fruluk Yeterli Olmayabilir? 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