{"id":1157,"date":"2023-07-27T11:31:28","date_gmt":"2023-07-27T11:31:28","guid":{"rendered":"https:\/\/statorials.org\/tr\/lojistik-regresyon-pitonu\/"},"modified":"2023-07-27T11:31:28","modified_gmt":"2023-07-27T11:31:28","slug":"lojistik-regresyon-pitonu","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/lojistik-regresyon-pitonu\/","title":{"rendered":"Python&#39;da lojistik regresyon nas\u0131l ger\u00e7ekle\u015ftirilir (ad\u0131m ad\u0131m)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/tr\/lojistik-regresyon-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">Lojistik regresyon,<\/a> <a href=\"https:\/\/statorials.org\/tr\/degiskenleri-aciklayici-yanitlar\/\" target=\"_blank\" rel=\"noopener noreferrer\">yan\u0131t de\u011fi\u015fkeni<\/a> ikili oldu\u011funda bir regresyon modeline uymak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Lojistik regresyon, a\u015fa\u011f\u0131daki formdaki bir denklemi bulmak i\u00e7in <em>maksimum olabilirlik tahmini<\/em> olarak bilinen bir y\u00f6ntemi kullan\u0131r:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>log[p(X) \/ ( <sub>1<\/sub> -p(X))] = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> X <sub>1<\/sub> + \u03b2 <sub>2<\/sub> X <sub>2<\/sub> + \u2026 + \u03b2 <sub>p<\/sub><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Alt\u0131n:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>X <sub>j<\/sub><\/strong> : <sup>j&#8217;inci<\/sup> tahmin de\u011fi\u015fkeni<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u03b2 <sub>j<\/sub><\/strong> : <sup>j&#8217;inci<\/sup> yorday\u0131c\u0131 de\u011fi\u015fkenin katsay\u0131s\u0131n\u0131n tahmini<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Denklemin sa\u011f taraf\u0131ndaki form\u00fcl, yan\u0131t de\u011fi\u015fkeninin 1 de\u011ferini almas\u0131na ili\u015fkin <strong>log olas\u0131l\u0131\u011f\u0131n\u0131<\/strong> tahmin eder.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Dolay\u0131s\u0131yla, bir lojistik regresyon modeli uydurdu\u011fumuzda, belirli bir g\u00f6zlemin 1 de\u011ferini alma olas\u0131l\u0131\u011f\u0131n\u0131 hesaplamak i\u00e7in a\u015fa\u011f\u0131daki denklemi kullanabiliriz:<\/span><\/p>\n<p> <span style=\"color: #000000;\">p(X) = e <sup>\u03b2 <sub>0<\/sub> + <sub>\u03b2<\/sub> <sub>1<\/sub> <sub>X<\/sub> <sub>1<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> <sup><sub>2<\/sub> <sub>X<\/sub> <sub>2<\/sub> <sub>+<\/sub> <sub>\u2026<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> p<\/span><\/p>\n<p> <span style=\"color: #000000;\">Daha sonra g\u00f6zlemi 1 veya 0 olarak s\u0131n\u0131fland\u0131rmak i\u00e7in belirli bir olas\u0131l\u0131k e\u015fi\u011fi kullan\u0131r\u0131z.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u00d6rne\u011fin olas\u0131l\u0131\u011f\u0131 0,5&#8217;ten b\u00fcy\u00fck veya ona e\u015fit olan g\u00f6zlemlerin &#8220;1&#8221;, di\u011fer t\u00fcm g\u00f6zlemlerin ise &#8220;0&#8221; olarak s\u0131n\u0131fland\u0131r\u0131laca\u011f\u0131n\u0131 s\u00f6yleyebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu e\u011fitimde, R&#8217;de lojistik regresyonun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fine ili\u015fkin ad\u0131m ad\u0131m bir \u00f6rnek sunulmaktad\u0131r.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ad\u0131m 1: Gerekli paketleri i\u00e7e aktar\u0131n<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u00d6ncelikle Python&#8217;da lojistik regresyon ger\u00e7ekle\u015ftirmek i\u00e7in gerekli paketleri i\u00e7e aktaraca\u011f\u0131z:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LogisticRegression\n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> metrics\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>2. Ad\u0131m: Verileri y\u00fckleyin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Bu \u00f6rnek i\u00e7in, <a href=\"https:\/\/www.ime.unicamp.br\/~dias\/Intoduction%20to%20Statistical%20Learning.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Introduction to Statistical Learning<\/a> kitab\u0131ndaki <strong>varsay\u0131lan<\/strong> veri k\u00fcmesini kullanaca\u011f\u0131z. Veri k\u00fcmesinin \u00f6zetini y\u00fcklemek ve g\u00f6r\u00fcnt\u00fclemek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#import dataset from CSV file on Github\n<span style=\"color: #000000;\">url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/default.csv\"\ndata = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n<\/span><\/span>\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>data[0:6]\n\n        default student balance income\n0 0 0 729.526495 44361.625074\n1 0 1 817.180407 12106.134700\n2 0 0 1073.549164 31767.138947\n3 0 0 529.250605 35704.493935\n4 0 0 785.655883 38463.495879\n5 0 1 919.588530 7491.558572  \n\n<span style=\"color: #008080;\">#find total observations in dataset<\/span>\nlen( <span style=\"color: #3366ff;\">data.index<\/span> )\n\n10000\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Bu veri seti 10.000 ki\u015fiye ili\u015fkin a\u015fa\u011f\u0131daki bilgileri i\u00e7ermektedir:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>Varsay\u0131lan:<\/strong> Bir ki\u015finin temerr\u00fcde d\u00fc\u015f\u00fcp d\u00fc\u015fmedi\u011fini g\u00f6sterir.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u00d6\u011frenci:<\/strong> Bireyin \u00f6\u011frenci olup olmad\u0131\u011f\u0131n\u0131 belirtir.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>bakiye:<\/strong> Bir bireyin ta\u015f\u0131d\u0131\u011f\u0131 ortalama bakiye.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>gelir:<\/strong> Bireyin geliri.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Belirli bir bireyin temerr\u00fcde d\u00fc\u015fme olas\u0131l\u0131\u011f\u0131n\u0131 tahmin eden bir lojistik regresyon modeli olu\u015fturmak i\u00e7in \u00f6\u011frenci durumunu, banka bakiyesini ve geliri kullanaca\u011f\u0131z.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>3. Ad\u0131m: E\u011fitim ve test \u00f6rnekleri olu\u015fturun<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Daha sonra veri setini, modeli <em>e\u011fitmek<\/em> i\u00e7in bir e\u011fitim setine ve modeli <em>test etmek i\u00e7in<\/em> bir test setine b\u00f6lece\u011fiz.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define the predictor variables and the response variable\n<\/span>X = data[[' <span style=\"color: #008000;\">student<\/span> ',' <span style=\"color: #008000;\">balance<\/span> ',' <span style=\"color: #008000;\">income<\/span> ']]\ny = data[' <span style=\"color: #008000;\">default<\/span> ']\n\n<span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span>X_train,X_test,y_train,y_test = <span style=\"color: #3366ff;\">train_test_split<\/span> (X,y,test_size=0.3,random_state=0)<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Ad\u0131m 4: Lojistik regresyon modelini yerle\u015ftirin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Daha sonra, lojistik regresyon modelini veri k\u00fcmesine s\u0131\u011fd\u0131rmak i\u00e7in <b>LogisticRegression()<\/b> i\u015flevini kullanaca\u011f\u0131z:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#instantiate the model\n<\/span>log_regression = LogisticRegression()\n\n<span style=\"color: #008080;\">#fit the model using the training data\n<\/span>log_regression. <span style=\"color: #3366ff;\">fit<\/span> (X_train,y_train)\n\n<span style=\"color: #008080;\">#use model to make predictions on test data\n<\/span>y_pred = log_regression. <span style=\"color: #3366ff;\">predict<\/span> (X_test)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Ad\u0131m 5: Model Tan\u0131lamas\u0131<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Regresyon modelini yerle\u015ftirdikten sonra modelimizin test veri seti \u00fczerindeki performans\u0131n\u0131 analiz edebiliriz.<\/span><\/p>\n<p> \u0130lk olarak <span style=\"color: #000000;\">model i\u00e7in<\/span> <span style=\"color: #000000;\">kar\u0131\u015f\u0131kl\u0131k matrisini olu\u015fturaca\u011f\u0131z<\/span> :<\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>cnf_matrix = metrics. <span style=\"color: #3366ff;\">confusion_matrix<\/span> (y_test, y_pred)\ncnf_matrix\n\narray([[2886, 1],\n       [113,0]])\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Kar\u0131\u015f\u0131kl\u0131k matrisinden \u015funu g\u00f6rebiliriz:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">#Do\u011fru olumlu tahminler: 2886<\/span><\/li>\n<li> <span style=\"color: #000000;\">#Do\u011fru olumsuz tahminler: 0<\/span><\/li>\n<li> <span style=\"color: #000000;\">#Yanl\u0131\u015f pozitif tahminler: 113<\/span><\/li>\n<li> <span style=\"color: #000000;\">#Yanl\u0131\u015f negatif tahminler: 1<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Ayr\u0131ca model taraf\u0131ndan yap\u0131lan d\u00fczeltme tahminlerinin y\u00fczdesini bize s\u00f6yleyen do\u011fruluk modelini de alabiliriz:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>print(\" <span style=\"color: #008000;\">Accuracy:<\/span> \", <span style=\"color: #3366ff;\">metrics.accuracy_score<\/span> (y_test, y_pred))l\n\nAccuracy: 0.962\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Bu bize modelin bir bireyin temerr\u00fcde d\u00fc\u015f\u00fcp d\u00fc\u015fmeyece\u011fi konusunda <strong>%96,2<\/strong> oran\u0131nda do\u011fru tahminde bulundu\u011funu g\u00f6steriyor.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Son olarak, tahmin olas\u0131l\u0131\u011f\u0131 e\u015fi\u011fi 1&#8217;den 0&#8217;a d\u00fc\u015f\u00fcr\u00fcld\u00fc\u011f\u00fcnde model taraf\u0131ndan tahmin edilen ger\u00e7ek pozitiflerin y\u00fczdesini g\u00f6r\u00fcnt\u00fcleyen Al\u0131c\u0131 \u00c7al\u0131\u015fma Karakteristi\u011fi (ROC) e\u011frisini \u00e7izebiliriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC (e\u011frinin alt\u0131ndaki alan) ne kadar y\u00fcksek olursa modelimiz sonu\u00e7lar\u0131 o kadar do\u011fru tahmin edebilir:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics<\/span>\ny_pred_proba = log_regression. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[::,1]\nfpr, tpr, _ = metrics. <span style=\"color: #3366ff;\">roc_curve<\/span> (y_test, y_pred_proba)\nauc = metrics. <span style=\"color: #3366ff;\">roc_auc_score<\/span> (y_test, y_pred_proba)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr,label=\" <span style=\"color: #008000;\">AUC=<\/span> \"+str(auc))\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=4)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/span><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11591 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/auc1.png\" alt=\"Python'da ROC e\u011frisi\" width=\"389\" height=\"262\" srcset=\"\" sizes=\"auto, \"><\/p>\n<div class=\"entry-content entry-content-single\" data-content-ads-inserted=\"true\">\n<p> <em><span style=\"color: #000000;\">Bu e\u011fitimde kullan\u0131lan Python kodunun tamam\u0131n\u0131 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/logistic_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">burada<\/a> bulabilirsiniz.<\/span><\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Lojistik regresyon, yan\u0131t de\u011fi\u015fkeni ikili oldu\u011funda bir regresyon modeline uymak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir. Lojistik regresyon, a\u015fa\u011f\u0131daki formdaki bir denklemi bulmak i\u00e7in maksimum olabilirlik tahmini olarak bilinen bir y\u00f6ntemi kullan\u0131r: log[p(X) \/ ( 1 -p(X))] = \u03b2 0 + \u03b2 1 X 1 + \u03b2 2 X 2 + \u2026 + \u03b2 p Alt\u0131n: X [&hellip;]<\/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-1157","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python&#039;da Lojistik Regresyon Nas\u0131l Ger\u00e7ekle\u015ftirilir (Ad\u0131m Ad\u0131m) - Statorials<\/title>\n<meta name=\"description\" content=\"Bu e\u011fitimde, ad\u0131m ad\u0131m bir \u00f6rnek de dahil olmak \u00fczere Python&#039;da lojistik regresyonun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r.\" \/>\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\/lojistik-regresyon-pitonu\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python&#039;da Lojistik Regresyon Nas\u0131l Ger\u00e7ekle\u015ftirilir (Ad\u0131m Ad\u0131m) - 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