{"id":1284,"date":"2023-07-27T00:47:36","date_gmt":"2023-07-27T00:47:36","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e4%b8%ad%e5%80%bc%e7%bb%9d%e5%af%b9%e5%81%8f%e5%b7%ae\/"},"modified":"2023-07-27T00:47:36","modified_gmt":"2023-07-27T00:47:36","slug":"python%e4%b8%ad%e7%9a%84%e4%b8%ad%e5%80%bc%e7%bb%9d%e5%af%b9%e5%81%8f%e5%b7%ae","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e4%b8%ad%e5%80%bc%e7%bb%9d%e5%af%b9%e5%81%8f%e5%b7%ae\/","title":{"rendered":"\u5982\u4f55\u5728python\u4e2d\u8ba1\u7b97\u7edd\u5bf9\u4e2d\u4f4d\u6570\u504f\u5dee"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\u4e2d\u503c\u7edd\u5bf9\u504f\u5dee\u8861\u91cf<\/strong>\u6570\u636e\u96c6\u4e2d<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u89c2\u5bdf\/\" target=\"_blank\" rel=\"noopener\">\u89c2\u6d4b<\/a>\u503c\u7684\u5206\u5e03\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u662f\u4e00\u4e2a\u7279\u522b\u6709\u7528\u7684\u5ea6\u91cf\uff0c\u56e0\u4e3a\u4e0e\u6807\u51c6\u5dee\u548c\u65b9\u5dee\u7b49\u5176\u4ed6\u79bb\u6563\u5ea6\u91cf\u76f8\u6bd4\uff0c\u5b83\u53d7\u5f02\u5e38\u503c\u7684\u5f71\u54cd\u8f83\u5c0f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7edd\u5bf9\u4e2d\u4f4d\u504f\u5dee\uff08\u901a\u5e38\u7f29\u5199\u4e3a MAD\uff09\u7684\u8ba1\u7b97\u516c\u5f0f\u4e3a\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>MAD = \u4e2d\u4f4d\u6570 (|x <sub>i<\/sub> \u2013 x <sub>m<\/sub> |)<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>x <sub>i<\/sub> \uff1a<\/strong>\u6570\u636e\u96c6\u7684<sup>\u7b2c i \u4e2a<\/sup>\u503c<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>x <sub>m<\/sub> \uff1a<\/strong>\u6570\u636e\u96c6\u4e2d\u7684\u4e2d\u503c<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528<a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">statsmodels<\/a> <strong>mad<\/strong>\u51fd\u6570\u5728 Python \u4e2d\u8ba1\u7b97\u4e2d\u503c\u7edd\u5bf9\u504f\u5dee\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 1\uff1a\u8ba1\u7b97\u8868\u7684 MAD<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u5355\u4e2a NumPy \u6570\u7ec4\u7684\u4e2d\u503c\u7edd\u5bf9\u504f\u5dee\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> statsmodels <span style=\"color: #008000;\">import<\/span> robust<\/span>\n\n#define data\n<\/span>data = np. <span style=\"color: #3366ff;\">array<\/span> ([1, 4, 4, 7, 12, 13, 16, 19, 22, 24])\n\n<span style=\"color: #008080;\">#calculate MAD\n<\/span>robust. <span style=\"color: #3366ff;\">mad<\/span> (data)\n\n11.1195\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6570\u636e\u96c6\u7684\u4e2d\u503c\u7edd\u5bf9\u504f\u5dee\u4e3a<strong>11.1195<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0c\u7528\u4e8e\u8ba1\u7b97 MAD \u7684\u516c\u5f0f\u901a\u8fc7\u5c06\u7ed3\u679c\u7f29\u653e\u7ea6 0.67 \u500d\u6765\u8ba1\u7b97\u5047\u8bbe<a href=\"https:\/\/statorials.org\/cn\/\u6b63\u6001\u5206\u5e03-1\/\" target=\"_blank\" rel=\"noopener\">\u6b63\u6001\u5206\u5e03<\/a>\u7684\u6807\u51c6\u5dee\u7684\u7a33\u5065\u4f30\u8ba1\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8981\u907f\u514d\u4f7f\u7528\u6b64\u7f29\u653e\u56e0\u5b50\uff0c\u53ea\u9700\u6309\u5982\u4e0b\u65b9\u5f0f\u8bbe\u7f6e c = 1\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate MAD without scaling factor\n<\/span>robust. <span style=\"color: #3366ff;\">mad<\/span> (data, c=1)\n\n7.5<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b 2\uff1a\u8ba1\u7b97 DataFrame \u7684 MAD<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u8ba1\u7b97 pandas DataFrame \u4e2d\u5355\u5217\u7684 MAD\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#create pandas DataFrame\n<\/span>data = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ( <span style=\"color: #3366ff;\">np.random.randint<\/span> (0,10,size=(5,3)),columns <span style=\"color: #3366ff;\">=<\/span> [' <span style=\"color: #008000;\">A<\/span> ',' <span style=\"color: #008000;\">B<\/span> ',' <span style=\"color: #008000;\">C<\/span> '])\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>data\n\n        A B C\n0 5 8 9\n1 5 0 0\n2 1 7 6\n3 9 2 4\n4 5 2 4\n\n<span style=\"color: #008080;\">#calculate MAD for column <em>B<\/em>\n<span style=\"color: #000000;\">data[[' <span style=\"color: #008000;\">B<\/span> ']]. <span style=\"color: #3366ff;\">apply<\/span> (robust. <span style=\"color: #3366ff;\">mad<\/span> )\n\nB 2.965204\ndtype:float64<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><em>B<\/em>\u5217\u7684\u7edd\u5bf9\u4e2d\u503c\u504f\u5dee\u4e3a<strong>2.965204<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u7c7b\u4f3c\u7684\u8bed\u6cd5\u6765\u8ba1\u7b97 pandas DataFrame \u4e2d\u591a\u5217\u7684 MAD\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#calculate MAD for all columns<\/span>\ndata[[' <span style=\"color: #008000;\">A<\/span> ',' <span style=\"color: #008000;\">B<\/span> ',' <span style=\"color: #008000;\">C<\/span> ']]. <span style=\"color: #3366ff;\">apply<\/span> (robust. <span style=\"color: #3366ff;\">mad<\/span> )\n\nAt 0.000000\nB 2.965204\nC 2.965204\ndtype:float64\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">A \u5217\u7684\u7edd\u5bf9\u4e2d\u4f4d\u504f\u5dee\u4e3a<b>0<\/b> \uff0cB \u5217\u7684\u7edd\u5bf9\u4e2d\u4f4d\u504f\u5dee\u4e3a<strong>2.965204<\/strong> \uff0cC \u5217\u7684\u7edd\u5bf9\u4e2d\u4f4d\u504f\u5dee\u4e3a<b>2.965204<\/b> \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\/\u87d2\u86c7\u5361\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97MAPE<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u87d2\u86c7\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u5728Python\u4e2d\u8ba1\u7b97SMAPE<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5747\u65b9\u6839\u8bef\u5dee-python\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u7528 Python \u8ba1\u7b97 RMSE<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4e2d\u503c\u7edd\u5bf9\u504f\u5dee\u8861\u91cf\u6570\u636e\u96c6\u4e2d\u89c2\u6d4b\u503c\u7684\u5206\u5e03\u3002 \u8fd9\u662f\u4e00\u4e2a\u7279\u522b\u6709\u7528\u7684\u5ea6\u91cf\uff0c\u56e0\u4e3a\u4e0e\u6807\u51c6\u5dee\u548c\u65b9\u5dee\u7b49\u5176\u4ed6\u79bb\u6563\u5ea6\u91cf\u76f8\u6bd4\uff0c\u5b83\u53d7\u5f02\u5e38 [&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-1284","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\u5728Python\u4e2d\u8ba1\u7b97\u7edd\u5bf9\u4e2d\u4f4d\u6570\u504f\u5dee<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u51e0\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u8ba1\u7b97\u4e2d\u503c\u7edd\u5bf9\u504f\u5dee\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/cn\/python\u4e2d\u7684\u4e2d\u503c\u7edd\u5bf9\u504f\u5dee\/\" \/>\n<meta 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