{"id":2171,"date":"2023-07-23T09:57:36","date_gmt":"2023-07-23T09:57:36","guid":{"rendered":"https:\/\/statorials.org\/cn\/python-%e6%9b%b2%e7%ba%bf%e7%b2%be%e5%ba%a6%e5%9b%9e%e8%b0%83\/"},"modified":"2023-07-23T09:57:36","modified_gmt":"2023-07-23T09:57:36","slug":"python-%e6%9b%b2%e7%ba%bf%e7%b2%be%e5%ba%a6%e5%9b%9e%e8%b0%83","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python-%e6%9b%b2%e7%ba%bf%e7%b2%be%e5%ba%a6%e5%9b%9e%e8%b0%83\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u521b\u5efa\u7cbe\u786e\u53ec\u56de\u66f2\u7ebf"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/\u56de\u5f52\u4e0e\u5206\u7c7b\/\" target=\"_blank\" rel=\"noopener\">\u5206\u7c7b\u6a21\u578b<\/a>\u65f6\uff0c\u6211\u4eec\u7ecf\u5e38\u7528\u6765\u8bc4\u4f30\u6a21\u578b\u8d28\u91cf\u7684\u4e24\u4e2a\u6307\u6807\u662f\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u51c6\u786e\u6027<\/strong>\uff1a\u76f8\u5bf9\u4e8e\u603b\u9633\u6027\u9884\u6d4b\u7684\u6b63\u786e\u9633\u6027\u9884\u6d4b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8ba1\u7b97\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u51c6\u786e\u7387 = \u771f\u9633\u6027 \/\uff08\u771f\u9633\u6027 + \u5047\u9633\u6027\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>\u63d0\u9192<\/strong>\uff1a\u6839\u636e\u5b9e\u9645\u9633\u6027\u603b\u6570\u7ea0\u6b63\u9633\u6027\u9884\u6d4b<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8ba1\u7b97\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u63d0\u9192 = \u771f\u9633\u6027 \/\uff08\u771f\u9633\u6027 + \u5047\u9634\u6027\uff09<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u53ef\u89c6\u5316\u67d0\u4e2a\u6a21\u578b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u6761<strong>\u7cbe\u786e\u7387-\u53ec\u56de\u7387\u66f2\u7ebf<\/strong>\u3002<\/span><span style=\"color: #000000;\">\u8be5\u66f2\u7ebf\u663e\u793a\u4e86\u4e0d\u540c\u9608\u503c\u4e0b\u7cbe\u5ea6\u548c\u53ec\u56de\u7387\u4e4b\u95f4\u7684\u6743\u8861\u3002<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-20068\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/precisionrecall2.png\" alt=\"Python \u4e2d\u7684\u7cbe\u786e\u53ec\u56de\u66f2\u7ebf\" width=\"523\" height=\"416\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 Python \u4e2d\u4e3a\u903b\u8f91\u56de\u5f52\u6a21\u578b\u521b\u5efa\u7cbe\u5ea6\u53ec\u56de\u66f2\u7ebf\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c1\u6b65\uff1a\u5bfc\u5165\u5305<br \/><\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u5bfc\u5165\u5fc5\u8981\u7684\u5305\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> datasets\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: #3366ff;\">metrics<\/span> <span style=\"color: #008000;\">import<\/span> precision_recall_curve\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>\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\u521b\u5efa\u4e00\u4e2a\u6570\u636e\u96c6\u5e76\u4e3a\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;\">#create dataset with 5 predictor variables\n<\/span>X, y = datasets. <span style=\"color: #3366ff;\">make_classification<\/span> (n_samples= <span style=\"color: #008000;\">1000<\/span> ,\n                                    n_features= <span style=\"color: #008000;\">4<\/span> ,\n                                    n_informative= <span style=\"color: #008000;\">3<\/span> ,\n                                    n_redundant= <span style=\"color: #008000;\">1<\/span> ,\n                                    random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#split dataset into training and testing set\n<\/span>X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= <span style=\"color: #008000;\">.3<\/span> , random_state= <span style=\"color: #008000;\">0<\/span> )\n\n<span style=\"color: #008080;\">#fit logistic regression model to dataset\n<\/span>classifier = LogisticRegression()\nclassify. <span style=\"color: #3366ff;\">fit<\/span> (X_train, y_train)\n\n<span style=\"color: #008080;\">#use logistic regression model to make predictions\n<\/span>y_score = classify. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[:, <span style=\"color: #008000;\">1<\/span> ]<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 3 \u6b65\uff1a\u521b\u5efa\u7cbe\u786e\u7387-\u53ec\u56de\u7387\u66f2\u7ebf<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u8ba1\u7b97\u6a21\u578b\u7684\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\uff0c\u5e76\u521b\u5efa\u7cbe\u786e\u7387-\u53ec\u56de\u7387\u66f2\u7ebf\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#calculate precision and recall\n<\/span>precision, recall, thresholds = precision_recall_curve(y_test, y_score)\n\n<span style=\"color: #008080;\">#create precision recall curve\n<\/span>fig, ax = plt. <span style=\"color: #3366ff;\">subplots<\/span> ()\nax. <span style=\"color: #3366ff;\">plot<\/span> (recall, precision, color=' <span style=\"color: #ff0000;\">purple<\/span> ')\n\n<span style=\"color: #008080;\">#add axis labels to plot\n<\/span>ax. <span style=\"color: #3366ff;\">set_title<\/span> (' <span style=\"color: #ff0000;\">Precision-Recall Curve<\/span> ')\nax. <span style=\"color: #3366ff;\">set_ylabel<\/span> (' <span style=\"color: #ff0000;\">Precision<\/span> ')\nax. <span style=\"color: #3366ff;\">set_xlabel<\/span> (' <span style=\"color: #ff0000;\">Recall<\/span> ')\n\n<span style=\"color: #008080;\">#displayplot<\/span>\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-20068\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/precisionrecall2.png\" alt=\"Python \u4e2d\u7684\u7cbe\u786e\u53ec\u56de\u66f2\u7ebf\" width=\"548\" height=\"437\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">x \u8f74\u663e\u793a\u53ec\u56de\u7387\uff0cy \u8f74\u663e\u793a\u4e0d\u540c\u9608\u503c\u7684\u7cbe\u5ea6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\u968f\u7740\u53ec\u56de\u7387\u7684\u589e\u52a0\uff0c\u7cbe\u786e\u5ea6\u4f1a\u964d\u4f4e\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u4ee3\u8868\u4e86\u4e24\u4e2a\u6307\u6807\u4e4b\u95f4\u7684\u6298\u8877\u3002\u4e3a\u4e86\u63d0\u9ad8\u6a21\u578b\u7684\u53ec\u56de\u7387\uff0c\u7cbe\u5ea6\u5fc5\u987b\u964d\u4f4e\uff0c\u53cd\u4e4b\u4ea6\u7136\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/cn\/\u903b\u8f91\u56de\u5f52-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u903b\u8f91\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/python-\u77e9\u9635\u6df7\u6dc6\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728 Python \u4e2d\u521b\u5efa\u6df7\u6dc6\u77e9\u9635<\/a><br \/><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><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5728\u673a\u5668\u5b66\u4e60\u4e2d\u4f7f\u7528\u5206\u7c7b\u6a21\u578b\u65f6\uff0c\u6211\u4eec\u7ecf\u5e38\u7528\u6765\u8bc4\u4f30\u6a21\u578b\u8d28\u91cf\u7684\u4e24\u4e2a\u6307\u6807\u662f\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u3002 \u51c6\u786e\u6027\uff1a\u76f8\u5bf9\u4e8e\u603b\u9633\u6027\u9884\u6d4b\u7684\u6b63 [&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-2171","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u5728 Python 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