{"id":870,"date":"2023-07-28T11:53:32","date_gmt":"2023-07-28T11:53:32","guid":{"rendered":"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/"},"modified":"2023-07-28T11:53:32","modified_gmt":"2023-07-28T11:53:32","slug":"pythonda-vive-nasil-hesaplanir","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/","title":{"rendered":"Python'da vif nas\u0131l hesaplan\u0131r"},"content":{"rendered":"

<\/p>\n

\n

Regresyon analizinde \u00e7oklu ba\u011flant\u0131,<\/a> iki veya daha fazla a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenin regresyon modelinde benzersiz veya ba\u011f\u0131ms\u0131z bilgi sa\u011flamayacak \u015fekilde birbiriyle y\u00fcksek d\u00fczeyde korelasyona sahip olmas\u0131 durumunda ortaya \u00e7\u0131kar.<\/span><\/p>\n

De\u011fi\u015fkenler aras\u0131ndaki korelasyon derecesi yeterince y\u00fcksekse, bu durum regresyon modelinin yerle\u015ftirilmesinde ve yorumlanmas\u0131nda sorunlara neden olabilir.<\/span><\/p>\n

\u00c7oklu do\u011frusall\u0131\u011f\u0131 tespit etmenin bir yolu, bir regresyon modelinde<\/a> a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler aras\u0131ndaki korelasyonu ve korelasyonun g\u00fcc\u00fcn\u00fc \u00f6l\u00e7en, varyans enflasyon fakt\u00f6r\u00fc (VIF)<\/strong> olarak bilinen bir \u00f6l\u00e7\u00fcm kullanmakt\u0131r.<\/span><\/p>\n

Bu e\u011fitimde Python’da VIF’nin nas\u0131l hesaplanaca\u011f\u0131 a\u00e7\u0131klanmaktad\u0131r.<\/span><\/p>\n

\u00d6rnek: Python’da VIF’yi hesaplama<\/strong><\/span><\/h2>\n

Bu \u00f6rnek i\u00e7in 10 basketbolcunun \u00f6zelliklerini a\u00e7\u0131klayan bir veri seti kullanaca\u011f\u0131z:<\/span><\/p>\n

 import<\/span> numpy as<\/span> np\nimport<\/span> pandas as<\/span> pd\n\n#create dataset<\/span>\ndf = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86],\n                   'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19],\n                   'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5],\n                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})\n\n#view dataset\n<\/span>df\n\n\trating points assists rebounds\n0 90 25 5 11\n1 85 20 7 8\n2 82 14 7 10\n3 88 16 8 6\n4 94 27 5 6\n5 90 20 7 9\n6 76 12 6 6\n7 75 15 9 10\n8 87 14 9 10\n9 86 19 5 7<\/strong><\/pre>\n
 Yan\u0131t de\u011fi\u015fkeni olarak puanlamay\u0131 ve a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler olarak say\u0131lar, asistler ve ribaundlar\u0131 kullanarak \u00e7oklu do\u011frusal regresyon modelini uydurmak istedi\u011fimizi varsayal\u0131m.<\/span><\/p>\n
 from<\/span> patsy import<\/span> damatrices\nfrom<\/span> statsmodels.stats.outliers_influence import<\/span> variance_inflation_factor\n\n#find design matrix for linear regression model using 'rating' as response variable<\/span> \ny, X = dmatrices('rating ~ points+assists+rebounds', data=df, return_type='dataframe')\n\n#calculate VIF for each explanatory variable<\/span>\nvivid = pd.DataFrame()\nvive['VIF'] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\nvivid['variable'] = X.columns\n\n#view VIF for each explanatory variable<\/span> \nlively\n\n\t       Variable VIF\n0 101.258171 Intercept\n1 1.763977 points\n2 1.959104 assists\n3 1.175030 rebounds<\/strong><\/pre>\n
 A\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenlerin her biri i\u00e7in VIF de\u011ferlerini g\u00f6zlemleyebiliriz:<\/span><\/p>\n
    \n
  •  puan:<\/strong> 1.76<\/span><\/li>\n
  •  asist:<\/strong> 1.96<\/span><\/li>\n
  •  ribaund:<\/strong> 1.18<\/span><\/li>\n<\/ul>\n
     Not:<\/strong> Bu de\u011fer konuyla ilgili olmad\u0131\u011f\u0131ndan \u015fablondaki “Kesi\u015fme” i\u00e7in VIF’yi dikkate almay\u0131n.<\/em><\/span><\/p>\n
     VIF de\u011ferleri nas\u0131l yorumlan\u0131r?<\/strong><\/h2>\n
     VIF de\u011feri 1’den ba\u015flar ve \u00fcst s\u0131n\u0131r\u0131 yoktur. VIF’leri yorumlaman\u0131n genel kural\u0131 \u015fudur:<\/span><\/p>\n
      \n
    •  1 de\u011feri, belirli bir a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken ile modeldeki di\u011fer herhangi bir a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken aras\u0131nda bir korelasyon olmad\u0131\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/li>\n
    •  1 ile 5 aras\u0131ndaki bir de\u011fer, belirli bir a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken ile modeldeki di\u011fer a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler aras\u0131nda orta d\u00fczeyde bir korelasyon oldu\u011funu g\u00f6sterir, ancak bu genellikle \u00f6zel dikkat gerektirecek kadar \u015fiddetli de\u011fildir.<\/span><\/li>\n
    •  5’ten b\u00fcy\u00fck bir de\u011fer, belirli bir a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fken ile modeldeki di\u011fer a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler aras\u0131nda potansiyel olarak ciddi bir korelasyon oldu\u011funu g\u00f6sterir. Bu durumda regresyon sonu\u00e7lar\u0131ndaki katsay\u0131 tahminleri ve p de\u011ferleri muhtemelen g\u00fcvenilmez olacakt\u0131r.<\/span><\/li>\n<\/ul>\n
       Regresyon modelimizde a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenlerin VIF de\u011ferlerinin her biri 1’e kapal\u0131 oldu\u011fundan \u00f6rne\u011fimizde \u00e7oklu do\u011frusall\u0131k sorun te\u015fkil etmemektedir.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
      Regresyon analizinde \u00e7oklu ba\u011flant\u0131, iki veya daha fazla a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenin regresyon modelinde benzersiz veya ba\u011f\u0131ms\u0131z bilgi sa\u011flamayacak \u015fekilde birbiriyle y\u00fcksek d\u00fczeyde korelasyona sahip olmas\u0131 durumunda ortaya \u00e7\u0131kar. De\u011fi\u015fkenler aras\u0131ndaki korelasyon derecesi yeterince y\u00fcksekse, bu durum regresyon modelinin yerle\u015ftirilmesinde ve yorumlanmas\u0131nda sorunlara neden olabilir. \u00c7oklu do\u011frusall\u0131\u011f\u0131 tespit etmenin bir yolu, bir regresyon modelinde a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenler aras\u0131ndaki […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-870","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\nPython'da VIF Nas\u0131l Hesaplan\u0131r \u2013 Statorials<\/title>\n<meta name=\"description\" content=\"Python'da VIF'nin (Varyans Enflasyon Fakt\u00f6r\u00fc) nas\u0131l hesaplanaca\u011f\u0131na dair basit bir a\u00e7\u0131klama.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python'da VIF Nas\u0131l Hesaplan\u0131r \u2013 Statorials\" \/>\n<meta property=\"og:description\" content=\"Python'da VIF'nin (Varyans Enflasyon Fakt\u00f6r\u00fc) nas\u0131l hesaplanaca\u011f\u0131na dair basit bir a\u00e7\u0131klama.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T11:53:32+00:00\" \/>\n<meta name=\"author\" content=\"Dr.benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Yazan:\" \/>\n\t<meta name=\"twitter:data1\" content=\"Dr.benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Tahmini okuma s\u00fcresi\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 dakika\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/\",\"url\":\"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/\",\"name\":\"Python'da VIF Nas\u0131l Hesaplan\u0131r \u2013 Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/tr\/#website\"},\"datePublished\":\"2023-07-28T11:53:32+00:00\",\"dateModified\":\"2023-07-28T11:53:32+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/tr\/#\/schema\/person\/365dc158a39a7c8ae256355451e3de48\"},\"description\":\"Python'da VIF'nin (Varyans Enflasyon Fakt\u00f6r\u00fc) nas\u0131l hesaplanaca\u011f\u0131na dair basit bir a\u00e7\u0131klama.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/#breadcrumb\"},\"inLanguage\":\"tr\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/tr\/pythonda-vive-nasil-hesaplanir\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Ev\",\"item\":\"https:\/\/statorials.org\/tr\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Python'da vif nas\u0131l hesaplan\u0131r\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/tr\/#website\",\"url\":\"https:\/\/statorials.org\/tr\/\",\"name\":\"Statorials\",\"description\":\"\u0130statistik okuryazarl\u0131\u011f\u0131 rehberiniz!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/tr\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"tr\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/tr\/#\/schema\/person\/365dc158a39a7c8ae256355451e3de48\",\"name\":\"Dr.benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"tr\",\"@id\":\"https:\/\/statorials.org\/tr\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/statorials.org\/tr\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"https:\/\/statorials.org\/tr\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Dr.benjamin anderson\"},\"description\":\"Merhaba, ben Benjamin, emekli bir istatistik profes\u00f6r\u00fc ve Statorials \u00f6\u011fretmenine d\u00f6n\u00fc\u015ft\u00fcm. \u0130statistik alan\u0131ndaki kapsaml\u0131 deneyimim ve uzmanl\u0131\u011f\u0131mla, \u00f6\u011frencilerimi Statorials arac\u0131l\u0131\u011f\u0131yla g\u00fc\u00e7lendirmek i\u00e7in bilgilerimi payla\u015fmaya can at\u0131yorum. 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 Modeldeki her a\u00e7\u0131klay\u0131c\u0131 de\u011fi\u015fkenin VIF’sini hesaplamak i\u00e7in statsmodels k\u00fct\u00fcphanesindeki variance_inflation_factor() fonksiyonunu<\/a> kullanabiliriz:<\/span><\/p>\n