{"id":2167,"date":"2023-07-23T10:04:03","date_gmt":"2023-07-23T10:04:03","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%aeauc\/"},"modified":"2023-07-23T10:04:03","modified_gmt":"2023-07-23T10:04:03","slug":"python%e3%81%aeauc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%aeauc\/","title":{"rendered":"Python \u3067 auc (\u66f2\u7dda\u4e0b\u9762\u7a4d) \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u30ed\u30b7\u3099\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u306f\u3001<\/a>\u5fdc\u7b54\u5909\u6570\u304c\u30d0\u30a4\u30ca\u30ea\u306e\u5834\u5408\u306b\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u7d71\u8a08\u624b\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3057\u3066\u3044\u308b\u304b\u3092\u8a55\u4fa1\u3059\u308b\u306b\u306f\u3001\u6b21\u306e 2 \u3064\u306e\u6307\u6a19\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u611f\u5ea6:<\/strong>\u7d50\u679c\u304c\u5b9f\u969b\u306b\u80af\u5b9a\u7684\u306a\u5834\u5408\u306b\u3001\u30e2\u30c7\u30eb\u304c\u89b3\u6e2c\u5024\u306b\u5bfe\u3057\u3066\u80af\u5b9a\u7684\u306a\u7d50\u679c\u3092\u4e88\u6e2c\u3059\u308b\u78ba\u7387\u3002\u3053\u308c\u306f\u300c\u771f\u967d\u6027\u7387\u300d\u3068\u3082\u547c\u3070\u308c\u307e\u3059\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u7279\u7570\u6027:<\/strong>\u7d50\u679c\u304c\u5b9f\u969b\u306b\u306f\u9670\u6027\u3067\u3042\u308b\u5834\u5408\u306b\u3001\u30e2\u30c7\u30eb\u304c\u89b3\u6e2c\u5024\u306b\u5bfe\u3057\u3066\u9670\u6027\u306e\u7d50\u679c\u3092\u4e88\u6e2c\u3059\u308b\u78ba\u7387\u3002\u3053\u308c\u306f\u300c\u771f\u306e\u30cd\u30ac\u30c6\u30a3\u30d6\u7387\u300d\u3068\u3082\u547c\u3070\u308c\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089 2 \u3064\u306e\u6e2c\u5b9a\u5024\u3092\u8996\u899a\u5316\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u300c\u53d7\u4fe1\u6a5f\u52d5\u4f5c\u7279\u6027\u300d\u66f2\u7dda\u3092\u8868\u3059<strong>ROC \u66f2\u7dda<\/strong>\u3092\u4f5c\u6210\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u306f\u3001\u611f\u5ea6\u3092 Y \u8ef8\u306b\u3001(1 \u2013 \u7279\u7570\u5ea6) \u3092 X \u8ef8\u306b\u793a\u3057\u305f\u30b0\u30e9\u30d5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u306e\u5206\u985e\u306b\u304a\u3051\u308b\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u6709\u52b9\u6027\u3092\u5b9a\u91cf\u5316\u3059\u308b 1 \u3064\u306e\u65b9\u6cd5\u306f\u3001\u300c\u66f2\u7dda\u306e\u4e0b\u306e\u9762\u7a4d\u300d\u3092\u8868\u3059<strong>AUC<\/strong>\u3092\u8a08\u7b97\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC \u304c 1 \u306b\u8fd1\u3065\u304f\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u512a\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u306f\u3001Python \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e AUC \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001Python \u3067\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">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<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u3001\u305d\u308c\u306b\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#import dataset from CSV file on Github\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/default.csv\"\ndata = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#define the predictor variables and the response variable\n<\/span>X = data[[' <span style=\"color: #ff0000;\">student<\/span> ',' <span style=\"color: #ff0000;\">balance<\/span> ',' <span style=\"color: #ff0000;\">income<\/span> ']]\ny = data[' <span style=\"color: #ff0000;\">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 = train_test_split(X,y,test_size= <span style=\"color: #008000;\">0.3<\/span> ,random_state= <span style=\"color: #008000;\">0<\/span> ) \n\n<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)<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: AUC \u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\"><strong>metrics.roc_auc_score()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30e2\u30c7\u30eb\u306e AUC \u3092\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#use model to predict probability that given y value is 1\n<\/span>y_pred_proba = log_regression. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[::, <span style=\"color: #008000;\">1<\/span> ]\n\n<span style=\"color: #008080;\">#calculate AUC of model\n<\/span>auc = metrics. <span style=\"color: #3366ff;\">roc_auc_score<\/span> (y_test, y_pred_proba)\n\n<span style=\"color: #008080;\">#print AUC score\n<\/span><span style=\"color: #008000;\">print<\/span> (auc)\n\n0.5602104030579559\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u7279\u5b9a\u306e\u30e2\u30c7\u30eb\u306e AUC (\u66f2\u7dda\u4e0b\u9762\u7a4d) \u306f<strong>0.5602<\/strong>\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC \u30b9\u30b3\u30a2\u304c<strong>0.5<\/strong>\u306e\u30e2\u30c7\u30eb\u306f\u3001\u30e9\u30f3\u30c0\u30e0\u306a\u63a8\u6e2c\u3092\u884c\u3046\u30e2\u30c7\u30eb\u3068\u540c\u7b49\u3067\u3042\u308b\u3053\u3068\u3092\u601d\u3044\u51fa\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3057\u305f\u304c\u3063\u3066\u3001\u307b\u3068\u3093\u3069\u306e\u5834\u5408\u3001AUC \u30b9\u30b3\u30a2\u304c<strong>0.5602<\/strong>\u306e\u30e2\u30c7\u30eb\u306f\u3001\u89b3\u6e2c\u5024\u3092\u6b63\u3057\u3044\u30af\u30e9\u30b9\u306b\u5206\u985e\u3059\u308b\u80fd\u529b\u304c\u4f4e\u3044\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001ROC \u66f2\u7dda\u3068 AUC \u30b9\u30b3\u30a2\u306b\u95a2\u3059\u308b\u8ffd\u52a0\u60c5\u5831\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ja\/\u5ca9\u306e\u66f2\u7dda\u3092\u89e3\u91c8\u3059\u308b\/\" target=\"_blank\" rel=\"noopener\">ROC \u66f2\u7dda\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5 (\u4f8b\u3042\u308a)<\/a><br \/> <a href=\"https:\/\/statorials.org\/ja\" target=\"_blank\" rel=\"noopener\">AUC \u30b9\u30b3\u30a2\u306f\u3069\u308c\u304f\u3089\u3044\u304c\u826f\u3044\u3068\u8003\u3048\u3089\u308c\u307e\u3059\u304b?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u306f\u3001\u5fdc\u7b54\u5909\u6570\u304c\u30d0\u30a4\u30ca\u30ea\u306e\u5834\u5408\u306b\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3059\u308b\u7d71\u8a08\u624b\u6cd5\u3067\u3059\u3002 \u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3057\u3066\u3044\u308b\u304b\u3092\u8a55\u4fa1\u3059\u308b\u306b\u306f\u3001\u6b21\u306e 2 \u3064\u306e\u6307\u6a19\u3092\u78ba\u8a8d\u3057\u307e\u3059\u3002 \u611f\u5ea6:\u7d50\u679c [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-2167","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u3067 AUC (\u66f2\u7dda\u4e0b\u9762\u7a4d) \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 - Statorials<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e AUC (\u66f2\u7dda\u4e0b\u9762\u7a4d) \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u3001\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u542b\u3081\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" 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(\u66f2\u7dda\u4e0b\u9762\u7a4d) \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 - Statorials\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ja\/#website\"},\"datePublished\":\"2023-07-23T10:04:03+00:00\",\"dateModified\":\"2023-07-23T10:04:03+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\"},\"description\":\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001R \u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e AUC (\u66f2\u7dda\u4e0b\u9762\u7a4d) 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