{"id":1200,"date":"2023-07-27T07:49:23","date_gmt":"2023-07-27T07:49:23","guid":{"rendered":"https:\/\/statorials.org\/tr\/pythonda-kement-regresyonu\/"},"modified":"2023-07-27T07:49:23","modified_gmt":"2023-07-27T07:49:23","slug":"pythonda-kement-regresyonu","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/pythonda-kement-regresyonu\/","title":{"rendered":"Python&#39;da kement regresyon (ad\u0131m ad\u0131m)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/tr\/kement-regresyonu\/\" target=\"_blank\" rel=\"noopener noreferrer\">Kement regresyonu,<\/a> verilerde <a href=\"https:\/\/statorials.org\/tr\/coklu-baglanti-regresyonu\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u00e7oklu ba\u011flant\u0131<\/a> mevcut oldu\u011funda regresyon modeline uyum sa\u011flamak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u00d6zetle, en k\u00fc\u00e7\u00fck kareler regresyonu, kalan kareler toplam\u0131n\u0131 (RSS) en aza indiren katsay\u0131 tahminlerini bulmaya \u00e7al\u0131\u015f\u0131r:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS = \u03a3(y <sub>ben<\/sub> \u2013 \u0177 <sub>ben<\/sub> )2<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Alt\u0131n:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> : <em>Toplam<\/em> anlam\u0131na gelen bir Yunan sembol\u00fc<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> : <sup>i&#8217;inci<\/sup> g\u00f6zlem i\u00e7in ger\u00e7ek yan\u0131t de\u011feri<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> : \u00c7oklu do\u011frusal regresyon modeline dayal\u0131 olarak tahmin edilen yan\u0131t de\u011feri<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Tersine, kement regresyonu a\u015fa\u011f\u0131dakileri en aza indirmeyi ama\u00e7lar:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS + \u03bb\u03a3| <sub>\u03b2j<\/sub> |<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">burada <em>j<\/em> 1&#8217;den <em>p<\/em> \u00f6ng\u00f6r\u00fcc\u00fc de\u011fi\u015fkenlere gider ve \u03bb \u2265 0&#8217;d\u0131r.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Denklemdeki bu ikinci terim <em>\u00e7ekilme cezas\u0131<\/em> olarak bilinir. Kement regresyonunda, \u03bb i\u00e7in m\u00fcmk\u00fcn olan en d\u00fc\u015f\u00fck MSE (ortalama kare hatas\u0131) testini \u00fcreten bir de\u011fer se\u00e7eriz.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu e\u011fitimde Python&#8217;da kement regresyonunun 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 kement regresyonu ger\u00e7ekle\u015ftirmek i\u00e7in gerekli paketleri i\u00e7e aktaraca\u011f\u0131z:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">from<\/span> numpy <span style=\"color: #008000;\">import<\/span> arange\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LassoCV\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold<\/strong><\/span><\/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 <strong>mtcars<\/strong> ad\u0131 verilen ve 33 farkl\u0131 araba hakk\u0131nda bilgi i\u00e7eren bir veri k\u00fcmesi kullanaca\u011f\u0131z. Yan\u0131t de\u011fi\u015fkeni olarak <strong>hp&#8217;yi<\/strong> ve yorday\u0131c\u0131lar olarak a\u015fa\u011f\u0131daki de\u011fi\u015fkenleri kullanaca\u011f\u0131z:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg<\/span><\/li>\n<li> <span style=\"color: #000000;\">a\u011f\u0131rl\u0131k<\/span><\/li>\n<li> <span style=\"color: #000000;\">bok<\/span><\/li>\n<li> <span style=\"color: #000000;\">qsec<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki kod bu veri k\u00fcmesinin nas\u0131l y\u00fcklenip g\u00f6r\u00fcnt\u00fclenece\u011fini g\u00f6sterir:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located<\/span>\nurl = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data<\/span>\ndata_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"wt\", \"drat\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data<\/span>\ndata[0:6]\n\n\tmpg wt drat qsec hp\n0 21.0 2.620 3.90 16.46 110\n1 21.0 2.875 3.90 17.02 110\n2 22.8 2.320 3.85 18.61 93\n3 21.4 3.215 3.08 19.44 110\n4 18.7 3,440 3.15 17.02 175\n5 18.1 3.460 2.76 20.22 105<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Ad\u0131m 3: Kement Regresyon Modelini Yerle\u015ftirin<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Daha sonra, kement regresyon modeline uyum sa\u011flamak i\u00e7in sklearn&#8217;in <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.RidgeCV.html\" target=\"_blank\" rel=\"noopener noreferrer\">LassoCV()<\/a> fonksiyonunu kullanaca\u011f\u0131z ve ceza terimi i\u00e7in kullan\u0131lacak en uygun alfa de\u011ferini bulmak amac\u0131yla k-katl\u0131 \u00e7apraz do\u011frulama ger\u00e7ekle\u015ftirmek i\u00e7in <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RepeatedKFold.html\" target=\"_blank\" rel=\"noopener noreferrer\">RepeatedKFold()<\/a> fonksiyonunu kullanaca\u011f\u0131z.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><em><strong>Not:<\/strong> Python&#8217;da &#8220;lambda&#8221; yerine &#8220;alfa&#8221; terimi kullan\u0131l\u0131r.<\/em><\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu \u00f6rnek i\u00e7in k = 10 kat se\u00e7ece\u011fiz ve \u00e7apraz do\u011frulama i\u015flemini 3 kez tekrarlayaca\u011f\u0131z.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ayr\u0131ca LassoCV() i\u015flevinin varsay\u0131lan olarak yaln\u0131zca 0,1, 1 ve 10 alfa de\u011ferlerini test etti\u011fini unutmay\u0131n. Ancak kendi alfa aral\u0131\u011f\u0131m\u0131z\u0131 0,01&#8217;lik art\u0131\u015flarla 0&#8217;dan 1&#8217;e kadar ayarlayabiliriz:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]]\ny = data[\"hp\"]\n\n<span style=\"color: #008080;\">#define cross-validation method to evaluate model\n<\/span>cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#define model\n<\/span>model = LassoCV(alphas= <span style=\"color: #3366ff;\">arange<\/span> (0, 1, 0.01), cv=cv, n_jobs=<\/strong><\/span> <span style=\"color: #008000;\"><strong>-1<\/strong><\/span> <span style=\"color: #000000;\"><strong>)\n\n<span style=\"color: #008080;\">#fit model\n<\/span>model. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n\n<span style=\"color: #008080;\">#display lambda that produced the lowest test MSE\n<\/span>print( <span style=\"color: #3366ff;\">model.alpha_<\/span> )\n\n0.99<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Testin MSE&#8217;sini en aza indiren lambda de\u011feri <strong>0,99<\/strong> olarak \u00e7\u0131k\u0131yor.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Ad\u0131m 4: Tahminlerde bulunmak i\u00e7in modeli kullan\u0131n<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Son olarak, yeni g\u00f6zlemler hakk\u0131nda tahminlerde bulunmak i\u00e7in son kement regresyon modelini kullanabiliriz. \u00d6rne\u011fin, a\u015fa\u011f\u0131daki kod, a\u015fa\u011f\u0131daki \u00f6zelliklere sahip yeni bir araban\u0131n nas\u0131l tan\u0131mlanaca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg: 24<\/span><\/li>\n<li> <span style=\"color: #000000;\">a\u011f\u0131rl\u0131k: 2,5<\/span><\/li>\n<li> <span style=\"color: #000000;\">fiyat: 3,5<\/span><\/li>\n<li> <span style=\"color: #000000;\">qsn: 18,5<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">A\u015fa\u011f\u0131daki kod, bu yeni g\u00f6zlemin <em>hp<\/em> de\u011ferini tahmin etmek i\u00e7in uygun kement regresyon modelinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define new observation\n<span style=\"color: #000000;\">new = [24, 2.5, 3.5, 18.5]\n<\/span>\n#predict hp value using lasso regression model\n<span style=\"color: #000000;\">model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n<\/span>\n<span style=\"color: #000000;\">array([105.63442071])\n<\/span><\/span><\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">Model, girilen de\u011ferlere g\u00f6re bu otomobilin <strong>105.63442071<\/strong> <em>hp<\/em> de\u011ferine sahip olaca\u011f\u0131n\u0131 \u00f6ng\u00f6r\u00fcyor.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Bu \u00f6rnekte kullan\u0131lan Python kodunun tamam\u0131n\u0131 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/lasso_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">burada<\/a> bulabilirsiniz.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Kement regresyonu, verilerde \u00e7oklu ba\u011flant\u0131 mevcut oldu\u011funda regresyon modeline uyum sa\u011flamak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir. \u00d6zetle, en k\u00fc\u00e7\u00fck kareler regresyonu, kalan kareler toplam\u0131n\u0131 (RSS) en aza indiren katsay\u0131 tahminlerini bulmaya \u00e7al\u0131\u015f\u0131r: RSS = \u03a3(y ben \u2013 \u0177 ben )2 Alt\u0131n: \u03a3 : Toplam anlam\u0131na gelen bir Yunan sembol\u00fc y i : i&#8217;inci g\u00f6zlem i\u00e7in ger\u00e7ek [&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-1200","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.3 - 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