{"id":1196,"date":"2023-07-27T08:14:05","date_gmt":"2023-07-27T08:14:05","guid":{"rendered":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e6%b3%a2%e5%b3%b0%e5%9b%9e%e5%bd%92\/"},"modified":"2023-07-27T08:14:05","modified_gmt":"2023-07-27T08:14:05","slug":"python-%e4%b8%ad%e7%9a%84%e6%b3%a2%e5%b3%b0%e5%9b%9e%e5%bd%92","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python-%e4%b8%ad%e7%9a%84%e6%b3%a2%e5%b3%b0%e5%9b%9e%e5%bd%92\/","title":{"rendered":"Python \u4e2d\u7684\u5cad\u56de\u5f52\uff08\u4e00\u6b65\u4e00\u6b65\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/\u5c71\u810a\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5cad\u56de\u5f52<\/a>\u662f\u5f53\u6570\u636e\u4e2d\u5b58\u5728<a href=\"https:\/\/statorials.org\/cn\/\u591a\u91cd\u5171\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u591a\u91cd\u5171\u7ebf\u6027<\/a>\u65f6\u6211\u4eec\u53ef\u4ee5\u7528\u6765\u62df\u5408\u56de\u5f52\u6a21\u578b\u7684\u65b9\u6cd5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7b80\u800c\u8a00\u4e4b\uff0c\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u8bd5\u56fe\u627e\u5230\u6700\u5c0f\u5316\u6b8b\u5dee\u5e73\u65b9\u548c (RSS) \u7684\u7cfb\u6570\u4f30\u8ba1\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS = \u03a3(y <sub>i<\/sub> \u2013 \u0177 <sub>i<\/sub> )2<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> \uff1a\u5e0c\u814a\u7b26\u53f7\uff0c\u610f\u601d\u662f<em>\u548c<\/em><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> \uff1a<sup>\u7b2c i \u4e2a<\/sup>\u89c2\u6d4b\u503c\u7684\u5b9e\u9645\u54cd\u5e94\u503c<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> \uff1a\u57fa\u4e8e\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u9884\u6d4b\u54cd\u5e94\u503c<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u76f8\u53cd\uff0c\u5cad\u56de\u5f52\u65e8\u5728\u6700\u5c0f\u5316\u4ee5\u4e0b\u56e0\u7d20\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS + \u03bb\u03a3\u03b2 <sub>j<\/sub> <sup>2<\/sup><\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5176\u4e2d<em>j<\/em>\u4ece 1 \u5230<em>p \u4e2a<\/em>\u9884\u6d4b\u53d8\u91cf\u4e14 \u03bb \u2265 0\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7b49\u5f0f\u4e2d\u7684\u7b2c\u4e8c\u9879\u79f0\u4e3a<em>\u63d0\u6b3e\u7f5a\u91d1<\/em>\u3002\u5728\u5cad\u56de\u5f52\u4e2d\uff0c\u6211\u4eec\u9009\u62e9\u4ea7\u751f\u5c3d\u53ef\u80fd\u6700\u4f4e\u7684 MSE \u68c0\u9a8c\uff08\u5747\u65b9\u8bef\u5dee\uff09\u7684 \u03bb \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5cad\u56de\u5f52\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c1\u6b65\uff1a\u5bfc\u5165\u5fc5\u8981\u7684\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\u5cad\u56de\u5f52\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">from<\/span> numpy <span style=\"color: #008000;\">import<\/span> arange\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> Ridge\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> RidgeCV\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c2\u6b65\uff1a\u52a0\u8f7d\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528\u540d\u4e3a<strong>mtcars<\/strong>\u7684\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b 33 \u8f86\u4e0d\u540c\u6c7d\u8f66\u7684\u4fe1\u606f\u3002\u6211\u4eec\u5c06\u4f7f\u7528<strong>hp<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff0c\u5e76\u4f7f\u7528\u4ee5\u4e0b\u53d8\u91cf\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u82f1\u91cc\/\u52a0\u4ed1<\/span><\/li>\n<li><span style=\"color: #000000;\">\u91cd\u91cf<\/span><\/li>\n<li><span style=\"color: #000000;\">\u62c9\u5c4e<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5feb\u79d2<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u52a0\u8f7d\u548c\u663e\u793a\u6b64\u6570\u636e\u96c6\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located<\/span>\nurl = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data<\/span>\ndata_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"wt\", \"drat\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data<\/span>\ndata[0:6]\n\n\tmpg wt drat qsec hp\n0 21.0 2.620 3.90 16.46 110\n1 21.0 2.875 3.90 17.02 110\n2 22.8 2.320 3.85 18.61 93\n3 21.4 3.215 3.08 19.44 110\n4 18.7 3,440 3.15 17.02 175\n5 18.1 3.460 2.76 20.22 105<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 3\uff1a\u62df\u5408\u5cad\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 sklearn \u7684<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.RidgeCV.html\" target=\"_blank\" rel=\"noopener noreferrer\">RidgeCV()<\/a>\u51fd\u6570\u6765\u62df\u5408\u5cad\u56de\u5f52\u6a21\u578b\uff0c\u5e76\u4f7f\u7528<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RepeatedKFold.html\" target=\"_blank\" rel=\"noopener noreferrer\">RepeatedKFold()<\/a>\u51fd\u6570\u6267\u884c k \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u4ee5\u627e\u5230\u7528\u4e8e\u60e9\u7f5a\u9879\u7684\u6700\u4f73 alpha \u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><em><strong>\u6ce8\u610f\uff1a<\/strong> Python \u4e2d\u4f7f\u7528\u672f\u8bed\u201calpha\u201d\u4ee3\u66ff\u201clambda\u201d\u3002<\/em><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u6b64\u793a\u4f8b\uff0c\u6211\u4eec\u5c06\u9009\u62e9 k = 10 \u500d\u5e76\u91cd\u590d\u4ea4\u53c9\u9a8c\u8bc1\u8fc7\u7a0b 3 \u6b21\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u53e6\u8bf7\u6ce8\u610f\uff0cRidgeCV() \u9ed8\u8ba4\u60c5\u51b5\u4e0b\u4ec5\u6d4b\u8bd5 alpha \u503c 0\u30011\u30011 \u548c 10\u3002\u4f46\u662f\uff0c\u6211\u4eec\u53ef\u4ee5\u5c06\u81ea\u5df1\u7684 alpha \u8303\u56f4\u8bbe\u7f6e\u4e3a 0 \u5230 1\uff0c\u589e\u91cf\u4e3a 0.01\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]]\ny = data[\"hp\"]\n\n<span style=\"color: #008080;\">#define cross-validation method to evaluate model\n<\/span>cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#define model\n<\/span>model = RidgeCV(alphas= <span style=\"color: #3366ff;\">arange<\/span> (0, 1, 0.01), cv=cv, scoring=' <span style=\"color: #008000;\">neg_mean_absolute_error<\/span> ')\n\n<span style=\"color: #008080;\">#fit model\n<\/span>model. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n\n<span style=\"color: #008080;\">#display lambda that produced the lowest test MSE\n<\/span>print( <span style=\"color: #3366ff;\">model.alpha_<\/span> )\n\n0.99<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u5c0f\u5316\u6d4b\u8bd5 MSE \u7684 lambda \u503c\u4e3a<strong>0.99<\/strong> \u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 4 \u6b65\uff1a\u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u6700\u7ec8\u7684\u5cad\u56de\u5f52\u6a21\u578b\u6765\u5bf9\u65b0\u7684\u89c2\u6d4b\u7ed3\u679c\u8fdb\u884c\u9884\u6d4b\u3002\u4f8b\u5982\uff0c\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u5982\u4f55\u5b9a\u4e49\u5177\u6709\u4ee5\u4e0b\u5c5e\u6027\u7684\u65b0\u8f66\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u82f1\u91cc\/\u52a0\u4ed1\uff1a24<\/span><\/li>\n<li><span style=\"color: #000000;\">\u91cd\u91cf\uff1a2.5<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4ef7\u683c\uff1a3.5<\/span><\/li>\n<li><span style=\"color: #000000;\">\u79d2\uff1a18.5<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528\u62df\u5408\u5cad\u56de\u5f52\u6a21\u578b\u6765\u9884\u6d4b\u8fd9\u4e2a\u65b0\u89c2\u6d4b\u503c\u7684<em>hp<\/em>\u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define new observation\n<span style=\"color: #000000;\">new = [24, 2.5, 3.5, 18.5]\n<\/span>\n#predict hp value using ridge regression model\n<span style=\"color: #000000;\">model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n\narray([104.16398018])\n<\/span><\/span><\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u6839\u636e\u8f93\u5165\u7684\u503c\uff0c\u6a21\u578b\u9884\u6d4b\u8fd9\u8f86\u8f66\u7684<em>\u9a6c\u529b<\/em>\u503c\u4e3a<strong>104.16398018<\/strong> \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5<a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/ridge_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u5728\u6b64\u5904<\/a>\u627e\u5230\u672c\u793a\u4f8b\u4e2d\u4f7f\u7528\u7684\u5b8c\u6574 Python \u4ee3\u7801\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5cad\u56de\u5f52\u662f\u5f53\u6570\u636e\u4e2d\u5b58\u5728\u591a\u91cd\u5171\u7ebf\u6027\u65f6\u6211\u4eec\u53ef\u4ee5\u7528\u6765\u62df\u5408\u56de\u5f52\u6a21\u578b\u7684\u65b9\u6cd5\u3002 \u7b80\u800c\u8a00\u4e4b\uff0c\u6700\u5c0f\u4e8c\u4e58\u56de\u5f52\u8bd5\u56fe\u627e\u5230\u6700\u5c0f\u5316\u6b8b\u5dee\u5e73\u65b9 [&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-1196","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized 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