{"id":14505,"date":"2025-03-07T19:45:15","date_gmt":"2025-03-07T16:45:15","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/dogrusal-regresyon-basitten-karmasiga-bir-bakis\/"},"modified":"2025-03-07T19:45:15","modified_gmt":"2025-03-07T16:45:15","slug":"dogrusal-regresyon-basitten-karmasiga-bir-bakis","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/dogrusal-regresyon-basitten-karmasiga-bir-bakis\/","title":{"rendered":"Do\u011frusal Regresyon: Basitten Karma\u015f\u0131\u011fa Bir Bak\u0131\u015f"},"content":{"rendered":"

<\/p>\n

Do\u011frusal Regresyon: Basitten Karma\u015f\u0131\u011fa Bir Bak\u0131\u015f<\/p>\n

Do\u011frusal regresyon, ba\u011f\u0131ms\u0131z de\u011fi\u015fkenler (\u00f6zellikler) ile ba\u011f\u0131ml\u0131 de\u011fi\u015fken (hedef) aras\u0131ndaki ili\u015fkiyi modellemek i\u00e7in kullan\u0131lan en temel ve yayg\u0131n makine \u00f6\u011frenmesi tekniklerinden biridir. Bu teknik, ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin de\u011ferini ba\u011f\u0131ms\u0131z de\u011fi\u015fkenlerin do\u011frusal bir fonksiyonu olarak tahmin etmeyi ama\u00e7lar. Basit\u00e7e ifade edersek, iki de\u011fi\u015fken aras\u0131ndaki ili\u015fkiyi bir do\u011fru ile temsil etmeye \u00e7al\u0131\u015f\u0131r\u0131z. Ancak, bu ili\u015fki her zaman m\u00fckemmel olmayabilir; baz\u0131 hatalar ve sapmalar g\u00f6r\u00fclebilir.<\/p>\n

Do\u011frusal Regresyonun \u00c7e\u015fitleri<\/h2>\n

Do\u011frusal regresyonun temelde iki t\u00fcr\u00fc vard\u0131r: basit do\u011frusal regresyon ve \u00e7oklu do\u011frusal regresyon. Basit do\u011frusal regresyonda, ba\u011f\u0131ml\u0131 de\u011fi\u015fken tek bir ba\u011f\u0131ms\u0131z de\u011fi\u015fkene ba\u011fl\u0131 olarak tahmin edilir. \u00d6rne\u011fin, bir \u00fcr\u00fcn\u00fcn sat\u0131\u015f miktar\u0131n\u0131 reklam harcamalar\u0131yla tahmin etmek gibi. \u00c7oklu do\u011frusal regresyonda ise, ba\u011f\u0131ml\u0131 de\u011fi\u015fken birden fazla ba\u011f\u0131ms\u0131z de\u011fi\u015fkene ba\u011fl\u0131 olarak tahmin edilir. \u00d6rne\u011fin, bir evin fiyat\u0131n\u0131 metrekaresi, konumu ve ya\u015f\u0131 gibi fakt\u00f6rlere ba\u011fl\u0131 olarak tahmin etmek gibi. Bu farkl\u0131l\u0131klar, modelin karma\u015f\u0131kl\u0131\u011f\u0131n\u0131 ve tahmin g\u00fcc\u00fcn\u00fc etkiler.<\/p>\n

Do\u011frusal Regresyon Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n

Do\u011frusal regresyon, veriler \u00fczerinden en uygun do\u011frusal ili\u015fkiyi bulmaya \u00e7al\u0131\u015f\u0131r. Bu i\u015flem, “en k\u00fc\u00e7\u00fck kareler y\u00f6ntemi” (ordinary least squares – OLS) kullan\u0131larak ger\u00e7ekle\u015ftirilir. OLS y\u00f6ntemi, tahmin edilen de\u011ferler ile ger\u00e7ek de\u011ferler aras\u0131ndaki farklar\u0131n karelerinin toplam\u0131n\u0131 (hata kareler toplam\u0131) minimize etmeyi hedefler. Bu y\u00f6ntemle, verileri en iyi temsil eden do\u011fru parametreleri (e\u011fim ve kesi\u015fim noktas\u0131) bulabiliriz. Sonu\u00e7 olarak, yeni veriler i\u00e7in ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin de\u011ferini tahmin edebiliriz.<\/p>\n

Python ile Do\u011frusal Regresyon Uygulamas\u0131<\/h2>\n

Python programlama dili ve Scikit-learn k\u00fct\u00fcphanesi, do\u011frusal regresyon modellerini uygulamak i\u00e7in olduk\u00e7a kullan\u0131\u015fl\u0131 ara\u00e7lard\u0131r. A\u015fa\u011f\u0131daki kod \u00f6rne\u011fi, basit bir do\u011frusal regresyon modelinin nas\u0131l olu\u015fturulaca\u011f\u0131n\u0131 g\u00f6stermektedir:<\/p>\n

\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\n\n# \u00d6rnek veriler\nX = np.array([[1], [2], [3]])\ny = np.array([2, 4, 5])\n\n# Model olu\u015fturma\nmodel = LinearRegression()\nmodel.fit(X, y)\n\n# Tahmin yapma\ntahmin = model.predict([[4]])\nprint(tahmin)\n<\/code><\/pre>\n
Bu kod,  verilen verilere g\u00f6re bir do\u011frusal regresyon modeli olu\u015fturur ve yeni bir veri noktas\u0131 i\u00e7in tahmini hesaplar.  Scikit-learn k\u00fct\u00fcphanesi, modelin e\u011fitimi ve de\u011ferlendirilmesi i\u00e7in bir\u00e7ok fonksiyon sunar.  Daha karma\u015f\u0131k modeller i\u00e7in de bu k\u00fct\u00fcphaneyi kullanabilirsiniz.  Elbette, daha detayl\u0131 analizler i\u00e7in  fatihsoysal.com<\/a>  sitesini ziyaret edebilirsiniz.<\/p>\n
Do\u011frusal Regresyonun Avantajlar\u0131 ve Dezavantajlar\u0131<\/h2>\n
Do\u011frusal regresyonun en b\u00fcy\u00fck avantaj\u0131, basitli\u011fi ve yorumlanabilirli\u011fidir.  Modelin parametreleri kolayca anla\u015f\u0131l\u0131r ve yorumlanabilir.  Ayr\u0131ca, hesaplama a\u00e7\u0131s\u0131ndan verimlidir ve b\u00fcy\u00fck veri setleri \u00fczerinde h\u0131zl\u0131 bir \u015fekilde \u00e7al\u0131\u015ft\u0131r\u0131labilir.  Ancak, do\u011frusal regresyonun baz\u0131 dezavantajlar\u0131 da vard\u0131r.  \u00d6rne\u011fin, verilerin do\u011frusal bir ili\u015fki g\u00f6stermesi gerekir.  E\u011fer veriler do\u011frusal de\u011filse, modelin do\u011frulu\u011fu d\u00fc\u015febilir.  Ayr\u0131ca, ayk\u0131r\u0131 de\u011ferlere kar\u015f\u0131 hassast\u0131r ve modelin performans\u0131n\u0131 olumsuz etkileyebilir.  Bu durumlarda, daha geli\u015fmi\u015f regresyon tekniklerine ba\u015fvurmak gerekebilir.<\/p>\n
Sonu\u00e7<\/h2>\n
Do\u011frusal regresyon, basitli\u011fi ve yayg\u0131n kullan\u0131m\u0131yla makine \u00f6\u011frenmesinin temel ta\u015flar\u0131ndan biridir.  Ancak, modelin s\u0131n\u0131rlamalar\u0131n\u0131 ve uygulamas\u0131n\u0131n dikkatlice yap\u0131lmas\u0131 gerekti\u011fini unutmamak \u00f6nemlidir.  Do\u011fru veri \u00f6n i\u015fleme ve model se\u00e7imi ile, do\u011frusal regresyon bir\u00e7ok uygulamada ba\u015far\u0131l\u0131 sonu\u00e7lar verebilir.  Bu makalede ele ald\u0131\u011f\u0131m\u0131z bilgiler, do\u011frusal regresyonun temellerini anlamak ve kendi projelerinizde kullanmak i\u00e7in size sa\u011flam bir temel olu\u015fturacakt\u0131r.  Daha fazla bilgi i\u00e7in,  buraya<\/a>  bakabilirsiniz.<\/p>\n
#Etiketler: Do\u011frusal Regresyon, Linear Regression, Regresyon Analizi, Makine \u00d6\u011frenmesi, \u0130statistik, Veri Bilimi, Python, R, Kod \u00d6rne\u011fi, Tahmin, Modelleme
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Bu teknik, ba\u011f\u0131ml\u0131 de\u011fi\u015fkenin de\u011ferini ba\u011f\u0131ms\u0131z de\u011fi\u015fkenlerin do\u011frusal bir fonksiyonu olarak tahmin etmeyi ama\u00e7lar. Basit\u00e7e ifade edersek, iki de\u011fi\u015fken aras\u0131ndaki ili\u015fkiyi bir do\u011fru ile temsil etmeye \u00e7al\u0131\u015f\u0131r\u0131z. 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