{"id":2170,"date":"2023-07-23T10:04:03","date_gmt":"2023-07-23T10:04:03","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e8%9f%92%e8%9b%87%e4%b8%ad%e7%9a%84auc\/"},"modified":"2023-07-23T10:04:03","modified_gmt":"2023-07-23T10:04:03","slug":"%e8%9f%92%e8%9b%87%e4%b8%ad%e7%9a%84auc","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e8%9f%92%e8%9b%87%e4%b8%ad%e7%9a%84auc\/","title":{"rendered":"\u5982\u4f55\u5728python\u4e2d\u8ba1\u7b97auc\uff08\u66f2\u7ebf\u4e0b\u9762\u79ef\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u903b\u8f91\u56de\u5f521\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u903b\u8f91\u56de\u5f52<\/a>\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5f53\u54cd\u5e94\u53d8\u91cf\u662f\u4e8c\u5143\u65f6\uff0c\u6211\u4eec\u7528\u5b83\u6765\u62df\u5408\u56de\u5f52\u6a21\u578b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u8bc4\u4f30\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5bf9\u6570\u636e\u96c6\u7684\u62df\u5408\u7a0b\u5ea6\uff0c\u6211\u4eec\u53ef\u4ee5\u67e5\u770b\u4ee5\u4e0b\u4e24\u4e2a\u6307\u6807\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u654f\u611f\u6027\uff1a<\/strong>\u5f53\u7ed3\u679c\u5b9e\u9645\u4e0a\u662f\u79ef\u6781\u7684\u65f6\uff0c\u6a21\u578b\u9884\u6d4b\u89c2\u5bdf\u7ed3\u679c\u4e3a\u79ef\u6781\u7684\u6982\u7387\u3002\u8fd9\u4e5f\u79f0\u4e3a\u201c\u771f\u9633\u6027\u7387\u201d\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u7279\u5f02\u6027\uff1a<\/strong>\u5f53\u7ed3\u679c\u5b9e\u9645\u4e0a\u4e3a\u8d1f\u65f6\uff0c\u6a21\u578b\u9884\u6d4b\u89c2\u5bdf\u7ed3\u679c\u4e3a\u8d1f\u7684\u6982\u7387\u3002\u8fd9\u4e5f\u79f0\u4e3a\u201c\u771f\u8d1f\u7387\u201d\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u53ef\u89c6\u5316\u8fd9\u4e24\u4e2a\u6d4b\u91cf\u503c\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u521b\u5efa<strong>ROC \u66f2\u7ebf<\/strong>\uff0c\u5b83\u4ee3\u8868\u201c\u63a5\u6536\u5668\u64cd\u4f5c\u7279\u6027\u201d\u66f2\u7ebf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8be5\u56fe\u6cbf y \u8f74\u663e\u793a\u7075\u654f\u5ea6\uff0c\u6cbf x \u8f74\u663e\u793a\uff081 \u2013 \u7279\u5f02\u6027\uff09\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u91cf\u5316\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5728\u6570\u636e\u5206\u7c7b\u65b9\u9762\u7684\u6709\u6548\u6027\u7684\u4e00\u79cd\u65b9\u6cd5\u662f\u8ba1\u7b97<strong>AUC<\/strong> \uff0c\u5b83\u4ee3\u8868\u201c\u66f2\u7ebf\u4e0b\u9762\u79ef\u201d\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC \u8d8a\u63a5\u8fd1 1\uff0c\u6a21\u578b\u8d8a\u597d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u903b\u8f91\u56de\u5f52\u6a21\u578b\u7684 AUC\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c1\u6b65\uff1a\u5bfc\u5165\u5305<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u5bfc\u5165\u5fc5\u8981\u7684\u5305\u4ee5\u5728 Python \u4e2d\u6267\u884c\u903b\u8f91\u56de\u5f52\uff1a<\/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>\u6b65\u9aa4 2\uff1a\u62df\u5408\u903b\u8f91\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u5bfc\u5165\u4e00\u4e2a\u6570\u636e\u96c6\u5e76\u5bf9\u5176\u62df\u5408\u903b\u8f91\u56de\u5f52\u6a21\u578b\uff1a<\/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>\u7b2c 3 \u6b65\uff1a\u8ba1\u7b97 AUC<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>metrics.roc_auc_score()<\/strong>\u51fd\u6570\u6765\u8ba1\u7b97\u6a21\u578b\u7684AUC\uff1a<\/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;\">\u6b64\u7279\u5b9a\u6a21\u578b\u7684 AUC\uff08\u66f2\u7ebf\u4e0b\u9762\u79ef\uff09\u4e3a<strong>0.5602<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56de\u60f3\u4e00\u4e0b\uff0cAUC \u5206\u6570\u4e3a<strong>0.5<\/strong>\u7684\u6a21\u578b\u5e76\u4e0d\u6bd4\u968f\u673a\u731c\u6d4b\u7684\u6a21\u578b\u66f4\u597d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u5728\u5927\u591a\u6570\u60c5\u51b5\u4e0b\uff0cAUC \u5206\u6570\u4e3a<strong>0.5602<\/strong>\u7684\u6a21\u578b\u5c06\u88ab\u8ba4\u4e3a\u5728\u5c06\u89c2\u5bdf\u7ed3\u679c\u5206\u7c7b\u5230\u6b63\u786e\u7684\u7c7b\u522b\u65b9\u9762\u8f83\u5dee\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u63d0\u4f9b\u6709\u5173 ROC \u66f2\u7ebf\u548c AUC \u5206\u6570\u7684\u5176\u4ed6\u4fe1\u606f\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u89e3\u91ca\u5ca9\u77f3\u66f2\u7ebf\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u91ca ROC \u66f2\u7ebf\uff08\u9644\u793a\u4f8b\uff09<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\" target=\"_blank\" rel=\"noopener\">\u4ec0\u4e48\u88ab\u8ba4\u4e3a\u662f\u826f\u597d\u7684 AUC \u5206\u6570\uff1f<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u903b\u8f91\u56de\u5f52\u662f\u4e00\u79cd\u7edf\u8ba1\u65b9\u6cd5\uff0c\u5f53\u54cd\u5e94\u53d8\u91cf\u662f\u4e8c\u5143\u65f6\uff0c\u6211\u4eec\u7528\u5b83\u6765\u62df\u5408\u56de\u5f52\u6a21\u578b\u3002 \u4e3a\u4e86\u8bc4\u4f30\u903b\u8f91\u56de\u5f52\u6a21\u578b\u5bf9\u6570\u636e\u96c6\u7684\u62df\u5408\u7a0b\u5ea6\uff0c [&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-2170","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - 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