<\/p>\n
Lojistik regresyon,<\/a> yan\u0131t de\u011fi\u015fkeni ikili oldu\u011funda bir regresyon modeline uymak i\u00e7in kulland\u0131\u011f\u0131m\u0131z istatistiksel bir y\u00f6ntemdir.<\/span><\/p>\n Lojistik regresyon modelinin bir veri k\u00fcmesine ne kadar iyi uydu\u011funu de\u011ferlendirmek i\u00e7in a\u015fa\u011f\u0131daki iki \u00f6l\u00e7\u00fcme bakabiliriz:<\/span><\/p>\n Bu iki \u00f6l\u00e7\u00fcm\u00fc g\u00f6rselle\u015ftirmenin bir yolu, “al\u0131c\u0131 \u00e7al\u0131\u015fma karakteristi\u011fi” e\u011frisi anlam\u0131na gelen bir ROC e\u011frisi<\/strong> olu\u015fturmakt\u0131r.<\/span><\/p>\n Bu, y ekseni boyunca duyarl\u0131l\u0131\u011f\u0131 ve x ekseni boyunca (1 \u2013 \u00f6zg\u00fcll\u00fc\u011f\u00fc) g\u00f6r\u00fcnt\u00fcleyen bir grafiktir.<\/span><\/p>\n Lojistik regresyon modelinin verileri s\u0131n\u0131fland\u0131rmadaki etkinli\u011fini \u00f6l\u00e7menin bir yolu, “e\u011frinin alt\u0131ndaki alan” anlam\u0131na gelen AUC’yi<\/strong> hesaplamakt\u0131r.<\/span><\/p>\n AUC 1’e ne kadar yak\u0131nsa model o kadar iyidir.<\/span><\/p>\n A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, Python’da bir lojistik regresyon modeli i\u00e7in AUC’nin nas\u0131l hesaplanaca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n \u00d6ncelikle Python’da lojistik regresyon ger\u00e7ekle\u015ftirmek i\u00e7in gerekli paketleri i\u00e7e aktaraca\u011f\u0131z:<\/span><\/p>\n Daha sonra, bir veri k\u00fcmesini i\u00e7e aktaraca\u011f\u0131z ve buna bir lojistik regresyon modeli yerle\u015ftirece\u011fiz:<\/span><\/p>\n Modelin AUC’sini hesaplamak i\u00e7in metrics.roc_auc_score()<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/p>\n Bu \u00f6zel model i\u00e7in AUC (e\u011fri alt\u0131ndaki alan) 0,5602’dir<\/strong> .<\/span><\/p>\n AUC puan\u0131 0,5<\/strong> olan bir modelin rastgele tahminler yapan bir modelden daha iyi olmad\u0131\u011f\u0131n\u0131 hat\u0131rlay\u0131n.<\/span><\/p>\n Dolay\u0131s\u0131yla \u00e7o\u011fu durumda, AUC puan\u0131 0,5602<\/strong> olan bir model, g\u00f6zlemleri do\u011fru s\u0131n\u0131flara s\u0131n\u0131fland\u0131rma konusunda zay\u0131f kabul edilecektir.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler ROC e\u011frileri ve AUC puanlar\u0131 hakk\u0131nda ek bilgi sa\u011flar:<\/span><\/p>\n ROC E\u011frisi Nas\u0131l Yorumlan\u0131r (\u00d6rneklerle)<\/a> Lojistik regresyon, yan\u0131t de\u011fi\u015fkeni ikili oldu\u011funda bir regresyon modeline uymak i\u00e7in kulland\u0131\u011f\u0131m\u0131z istatistiksel bir y\u00f6ntemdir. Lojistik regresyon modelinin bir veri k\u00fcmesine ne kadar iyi uydu\u011funu de\u011ferlendirmek i\u00e7in a\u015fa\u011f\u0131daki iki \u00f6l\u00e7\u00fcme bakabiliriz: Duyarl\u0131l\u0131k: Sonu\u00e7 ger\u00e7ekten olumluyken modelin bir g\u00f6zlem i\u00e7in olumlu bir sonu\u00e7 tahmin etme olas\u0131l\u0131\u011f\u0131. Buna ayn\u0131 zamanda \u201cger\u00e7ek pozitif oran\u201d da denir. \u00d6zg\u00fcll\u00fck: Sonu\u00e7 […]<\/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-2170","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n\n
1. Ad\u0131m: Paketleri i\u00e7e aktar\u0131n<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\nfrom<\/span> sklearn. linear_model<\/span> import<\/span> LogisticRegression\nfrom<\/span> sklearn import<\/span> metrics\n<\/strong><\/pre>\n Ad\u0131m 2: Lojistik regresyon modelini yerle\u015ftirin<\/strong><\/span><\/h3>\n
#import dataset from CSV file on Github\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/default.csv\"\ndata = pd. read_csv<\/span> (url)\n\n#define the predictor variables and the response variable\n<\/span>X = data[[' student<\/span> ',' balance<\/span> ',' income<\/span> ']]\ny = data[' default<\/span> ']\n\n#split the dataset into training (70%) and testing (30%) sets\n<\/span>X_train,X_test,y_train,y_test = train_test_split(X,y,test_size= 0.3<\/span> ,random_state= 0<\/span> ) \n\n#instantiate the model\n<\/span>log_regression = LogisticRegression()\n\n#fit the model using the training data\n<\/span>log_regression. fit<\/span> (X_train,y_train)<\/strong><\/span><\/pre>\n 3. Ad\u0131m: AUC’yi hesaplay\u0131n<\/strong><\/span><\/h3>\n
#use model to predict probability that given y value is 1\n<\/span>y_pred_proba = log_regression. predict_proba<\/span> (X_test)[::, 1<\/span> ]\n\n#calculate AUC of model\n<\/span>auc = metrics. roc_auc_score<\/span> (y_test, y_pred_proba)\n\n#print AUC score\n<\/span>print<\/span> (auc)\n\n0.5602104030579559\n<\/strong><\/span><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
\u0130yi bir AUC puan\u0131 nedir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"