{"id":1583,"date":"2023-07-25T18:37:40","date_gmt":"2023-07-25T18:37:40","guid":{"rendered":"https:\/\/statorials.org\/ko\/roc-%e1%84%80%e1%85%a9%e1%86%a8%e1%84%89%e1%85%a5%e1%86%ab-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%8e%e1%85%ae%e1%84%8c%e1%85%a5%e1%86%a8\/"},"modified":"2023-07-25T18:37:40","modified_gmt":"2023-07-25T18:37:40","slug":"roc-%e1%84%80%e1%85%a9%e1%86%a8%e1%84%89%e1%85%a5%e1%86%ab-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%8e%e1%85%ae%e1%84%8c%e1%85%a5%e1%86%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/roc-%e1%84%80%e1%85%a9%e1%86%a8%e1%84%89%e1%85%a5%e1%86%ab-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab-%e1%84%8e%e1%85%ae%e1%84%8c%e1%85%a5%e1%86%a8\/","title":{"rendered":"Python\uc5d0\uc11c roc \uace1\uc120\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95(\ub2e8\uacc4\ubcc4)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ko\/\u1105\u1169\u110c\u1175\u1109\u1173\u1110\u1175\u11a8-\u1112\u116c\u1100\u1171-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294<\/a> \uc751\ub2f5 \ubcc0\uc218\uac00 \uc774\uc9c4\uc77c \ub54c \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ud558\ub294 \ud1b5\uacc4 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc774 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uc5bc\ub9c8\ub098 \uc798 \ub9de\ub294\uc9c0 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ub2e4\uc74c \ub450 \uac00\uc9c0 \uce21\uc815\ud56d\ubaa9\uc744 \uc0b4\ud3b4\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\ubbfc\uac10\ub3c4:<\/strong> \uacb0\uacfc\uac00 \uc2e4\uc81c\ub85c \uae0d\uc815\uc801\uc77c \ub54c \ubaa8\ub378\uc774 \uad00\ucc30\uc5d0 \ub300\ud55c \uae0d\uc815\uc801\uc778 \uacb0\uacfc\ub97c \uc608\uce21\ud560 \ud655\ub960\uc785\ub2c8\ub2e4. \uc774\ub97c &#8216;\uc9c4\uc591\uc131\ub960&#8217;\uc774\ub77c\uace0\ub3c4 \ud569\ub2c8\ub2e4.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\ud2b9\uc774\uc131:<\/strong> \uacb0\uacfc\uac00 \uc2e4\uc81c\ub85c \ubd80\uc815\uc801\uc77c \ub54c \ubaa8\ub378\uc774 \uad00\ucc30\uc5d0 \ub300\ud574 \ubd80\uc815\uc801\uc778 \uacb0\uacfc\ub97c \uc608\uce21\ud560 \ud655\ub960\uc785\ub2c8\ub2e4. \uc774\ub97c &#8216;\uc9c4\uc74c\uc131\ub960&#8217;\uc774\ub77c\uace0\ub3c4 \ud569\ub2c8\ub2e4.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\uc774 \ub450 \uac00\uc9c0 \uce21\uc815\uac12\uc744 \uc2dc\uac01\ud654\ud558\ub294 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740 &#8220;\uc218\uc2e0\uae30 \uc791\ub3d9 \ud2b9\uc131&#8221; \uace1\uc120\uc744 \ub098\ud0c0\ub0b4\ub294 <strong>ROC \uace1\uc120\uc744<\/strong> \ub9cc\ub4dc\ub294 \uac83\uc785\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ubaa8\ub378\uc758 \ubbfc\uac10\ub3c4\uc640 \ud2b9\uc774\ub3c4\ub97c \ud45c\uc2dc\ud55c \uadf8\ub798\ud504\uc785\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ub2e8\uacc4\ubcc4 \uc608\uc81c\uc5d0\uc11c\ub294 Python\uc5d0\uc11c ROC \uace1\uc120\uc744 \ub9cc\ub4e4\uace0 \ud574\uc11d\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">1\ub2e8\uacc4: \ud544\uc694\ud55c \ud328\ud0a4\uc9c0 \uac00\uc838\uc624\uae30<\/span><\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\uba3c\uc800 Python\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc218\ud589\ud558\ub294 \ub370 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uac00\uc838\uc635\ub2c8\ub2e4.<\/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>2\ub2e8\uacc4: \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \uc801\ud569<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c \ub370\uc774\ud130\uc138\ud2b8\ub97c \uac00\uc838\uc640\uc11c \uc5ec\uae30\uc5d0 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc801\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/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>3\ub2e8\uacc4: ROC \uace1\uc120 \uadf8\ub9ac\uae30<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c, Matplotlib \ub370\uc774\ud130 \uc2dc\uac01\ud654 \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucc38\uc591\uc131\ub960\uacfc \uac70\uc9d3\uc591\uc131\ub960\uc744 \uacc4\uc0b0\ud558\uace0 ROC \uace1\uc120\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/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;\">\uace1\uc120\uc774 \ud50c\ub86f\uc758 \uc67c\ucabd \uc0c1\ub2e8 \ubaa8\uc11c\ub9ac\uc5d0 \uac00\uae4c\uc6b8\uc218\ub85d \ubaa8\ub378\uc774 \ub370\uc774\ud130\ub97c \ubc94\uc8fc\ub85c \ub354 \uc798 \ubd84\ub958\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc704 \uadf8\ub798\ud504\uc5d0\uc11c \ubcfc \uc218 \uc788\ub4ef\uc774 \uc774 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc740 \ub370\uc774\ud130\ub97c \uce74\ud14c\uace0\ub9ac\ub85c \uc815\ub82c\ud558\ub294 \uc791\uc5c5\uc774 \ub9e4\uc6b0 \uc88b\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774\ub97c \uc815\ub7c9\ud654\ud558\uae30 \uc704\ud574 \uace1\uc120 \uc544\ub798\uc5d0 \uc788\ub294 \ud50c\ub86f\uc758 \uc591\uc744 \uc54c\ub824\uc8fc\ub294 AUC(\uace1\uc120 \uc544\ub798 \uc601\uc5ed)\ub97c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC\uac00 1\uc5d0 \uac00\uae4c\uc6b8\uc218\ub85d \ubaa8\ub378\uc774 \ub354 \uc88b\uc2b5\ub2c8\ub2e4. AUC\uac00 0.5\uc778 \ubaa8\ub378\uc740 \ubb34\uc791\uc704 \ubd84\ub958\ub97c \uc218\ud589\ud558\ub294 \ubaa8\ub378\ubcf4\ub2e4 \ub098\uc744 \uac83\uc774 \uc5c6\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>4\ub2e8\uacc4: AUC \uacc4\uc0b0<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 AUC\ub97c \uacc4\uc0b0\ud558\uace0 \uc774\ub97c ROC \ud50c\ub86f\uc758 \uc624\ub978\ucabd \ud558\ub2e8\uc5d0 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/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;\">\uc774 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc758 AUC\ub294 <strong>0.5602<\/strong> \ub85c \ub098\ud0c0\ub0ac\uc2b5\ub2c8\ub2e4. \uc774 \uc218\uce58\ub294 0.5\uc5d0 \uac00\uae5d\uae30 \ub54c\ubb38\uc5d0 \ubaa8\ub378\uc774 \ub370\uc774\ud130\ub97c \uc81c\ub300\ub85c \ubd84\ub958\ud558\uc9c0 \ubabb\ud558\uace0 \uc788\uc74c\uc744 \ud655\uc778\uc2dc\ucf1c \uc90d\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\uad00\ub828 \ud56d\ubaa9:<\/strong> <a href=\"https:\/\/statorials.org\/ko\/\u110b\u1167\u1105\u1165-\u1100\u1169\u11a8\u1109\u1165\u11ab-\u1100\u1173\u1105\u1175\u1100\u1175-roc-python\/\">Python\uc5d0\uc11c \uc5ec\ub7ec ROC \uace1\uc120\uc744 \uadf8\ub9ac\ub294 \ubc29\ubc95<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc751\ub2f5 \ubcc0\uc218\uac00 \uc774\uc9c4\uc77c \ub54c \ud68c\uadc0 \ubaa8\ub378\uc744 \ub9de\ucd94\ub294 \ub370 \uc0ac\uc6a9\ud558\ub294 \ud1b5\uacc4 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc774 \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \uc5bc\ub9c8\ub098 \uc798 \ub9de\ub294\uc9c0 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ub2e4\uc74c \ub450 \uac00\uc9c0 \uce21\uc815\ud56d\ubaa9\uc744 \uc0b4\ud3b4\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubbfc\uac10\ub3c4: \uacb0\uacfc\uac00 \uc2e4\uc81c\ub85c \uae0d\uc815\uc801\uc77c \ub54c \ubaa8\ub378\uc774 \uad00\ucc30\uc5d0 \ub300\ud55c \uae0d\uc815\uc801\uc778 \uacb0\uacfc\ub97c \uc608\uce21\ud560 \ud655\ub960\uc785\ub2c8\ub2e4. \uc774\ub97c &#8216;\uc9c4\uc591\uc131\ub960&#8217;\uc774\ub77c\uace0\ub3c4 \ud569\ub2c8\ub2e4. \ud2b9\uc774\uc131: \uacb0\uacfc\uac00 \uc2e4\uc81c\ub85c \ubd80\uc815\uc801\uc77c \ub54c \ubaa8\ub378\uc774 \uad00\ucc30\uc5d0 \ub300\ud574 \ubd80\uc815\uc801\uc778 \uacb0\uacfc\ub97c [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-1583","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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