{"id":1582,"date":"2023-07-25T18:37:40","date_gmt":"2023-07-25T18:37:40","guid":{"rendered":"https:\/\/statorials.org\/ja\/roc-%e3%82%ab%e3%83%bc%e3%83%95%e3%82%99-python-%e3%82%92%e3%83%88%e3%83%ac%e3%83%bc%e3%82%b9%e3%81%99%e3%82%8b\/"},"modified":"2023-07-25T18:37:40","modified_gmt":"2023-07-25T18:37:40","slug":"roc-%e3%82%ab%e3%83%bc%e3%83%95%e3%82%99-python-%e3%82%92%e3%83%88%e3%83%ac%e3%83%bc%e3%82%b9%e3%81%99%e3%82%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/roc-%e3%82%ab%e3%83%bc%e3%83%95%e3%82%99-python-%e3%82%92%e3%83%88%e3%83%ac%e3%83%bc%e3%82%b9%e3%81%99%e3%82%8b\/","title":{"rendered":"Python \u3067 roc \u66f2\u7dda\u3092\u63cf\u304f\u65b9\u6cd5 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)"},"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\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\u3053\u308c\u306f\u3001\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u611f\u5ea6\u3068\u7279\u7570\u5ea6\u3092\u8868\u793a\u3059\u308b\u30b0\u30e9\u30d5\u3067\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 ROC \u66f2\u7dda\u3092\u4f5c\u6210\u3057\u3066\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">\u30b9\u30c6\u30c3\u30d7 1: \u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/span><\/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<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>\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=0.3,random_state=0) \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: ROC \u66f2\u7dda\u3092\u63cf\u304f<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u771f\u967d\u6027\u7387\u3068\u507d\u967d\u6027\u7387\u3092\u8a08\u7b97\u3057\u3001Matplotlib \u30c7\u30fc\u30bf\u8996\u899a\u5316\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u7528\u3057\u3066 ROC \u66f2\u7dda\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics\n<\/span>y_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)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr)\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">True Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">False Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-15772 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/rocpython1.png\" alt=\"\" width=\"399\" height=\"267\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u66f2\u7dda\u304c\u30d7\u30ed\u30c3\u30c8\u306e\u5de6\u4e0a\u9685\u306b\u8fd1\u3051\u308c\u3070\u8fd1\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u30c7\u30fc\u30bf\u3092\u3088\u308a\u9069\u5207\u306b\u30ab\u30c6\u30b4\u30ea\u306b\u5206\u985e\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e0a\u306e\u30b0\u30e9\u30d5\u304b\u3089\u308f\u304b\u308b\u3088\u3046\u306b\u3001\u3053\u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306f\u3001\u30c7\u30fc\u30bf\u3092\u30ab\u30c6\u30b4\u30ea\u30fc\u306b\u5206\u985e\u3059\u308b\u3068\u3044\u3046\u70b9\u3067\u304b\u306a\u308a\u4e0d\u5341\u5206\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u5b9a\u91cf\u5316\u3059\u308b\u306b\u306f\u3001AUC (\u66f2\u7dda\u306e\u4e0b\u306e\u9762\u7a4d) \u3092\u8a08\u7b97\u3057\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30d7\u30ed\u30c3\u30c8\u306e\u3069\u306e\u7a0b\u5ea6\u304c\u66f2\u7dda\u306e\u4e0b\u306b\u3042\u308b\u304b\u304c\u308f\u304b\u308a\u307e\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 AUC \u304c 0.5 \u306b\u7b49\u3057\u3044\u30e2\u30c7\u30eb\u306f\u3001\u30e9\u30f3\u30c0\u30e0\u306a\u5206\u985e\u3092\u884c\u3046\u30e2\u30c7\u30eb\u3068\u540c\u7b49\u3067\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 4: AUC \u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e AUC \u3092\u8a08\u7b97\u3057\u3001ROC \u30d7\u30ed\u30c3\u30c8\u306e\u53f3\u4e0b\u9685\u306b\u8868\u793a\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;\">#define metrics\n<\/span>y_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: #ff0000;\">AUC=<\/span> \"+str(auc))\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">True Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">False Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=4)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-15773 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/rocpython2.png\" alt=\"\" width=\"404\" height=\"275\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u306e AUC \u306f<strong>0.5602<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3053\u306e\u6570\u5024\u306f 0.5 \u306b\u8fd1\u3044\u305f\u3081\u3001\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u3092\u5206\u985e\u3059\u308b\u969b\u306b\u9069\u5207\u306b\u6a5f\u80fd\u3057\u3066\u3044\u306a\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u95a2\u9023:<\/strong> <a href=\"https:\/\/statorials.org\/ja\/roc-python\u3066\u3099\u8907\u6570\u306e\u66f2\u7dda\u3092\u63cf\u304f\/\">Python \u3067\u8907\u6570\u306e ROC \u66f2\u7dda\u3092\u30d7\u30ed\u30c3\u30c8\u3059\u308b\u65b9\u6cd5<\/a><\/span><\/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\u304c [&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-1582","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 ROC \u66f2\u7dda\u3092\u63cf\u304f\u65b9\u6cd5 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7) - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067 ROC \u66f2\u7dda\u3092\u63cf\u753b\u3059\u308b\u65b9\u6cd5\u3092\u30b9\u30c6\u30c3\u30d7\u3054\u3068\u306e\u4f8b\u3068\u3068\u3082\u306b\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, 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