{"id":4508,"date":"2023-07-10T13:36:46","date_gmt":"2023-07-10T13:36:46","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%88%97%e8%bd%a6%e6%8e%a7%e5%88%b6%e7%b3%bb%e7%bb%9f\/"},"modified":"2023-07-10T13:36:46","modified_gmt":"2023-07-10T13:36:46","slug":"%e5%88%97%e8%bd%a6%e6%8e%a7%e5%88%b6%e7%b3%bb%e7%bb%9f","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%88%97%e8%bd%a6%e6%8e%a7%e5%88%b6%e7%b3%bb%e7%bb%9f\/","title":{"rendered":"A\uff1a\u5982\u4f55\u4f7f\u7528traincontrol\u6765\u63a7\u5236\u8bad\u7ec3\u53c2\u6570"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u8bc4\u4f30\u6a21\u578b\u5bf9\u6570\u636e\u96c6\u7684\u62df\u5408\u7a0b\u5ea6\uff0c\u6211\u4eec\u9700\u8981\u5206\u6790\u5176\u5728\u4ee5\u524d\u4ece\u672a\u89c1\u8fc7\u7684\u89c2\u5bdf\u7ed3\u679c\u4e0a\u7684\u8868\u73b0\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b9e\u73b0\u6b64\u76ee\u7684\u7684\u6700\u5e38\u89c1\u65b9\u6cd5\u4e4b\u4e00\u662f\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/k\u6298\u4ea4\u53c9\u9a8c\u8bc1\/\" target=\"_blank\" rel=\"noopener noreferrer\">k \u6298\u4ea4\u53c9\u9a8c\u8bc1<\/a>\uff0c\u5b83\u4f7f\u7528\u4ee5\u4e0b\u65b9\u6cd5\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u5c06\u6570\u636e\u96c6\u968f\u673a\u5206\u4e3a<em>k<\/em>\u7ec4\uff0c\u6216\u201c\u6298\u53e0\u201d\uff0c\u5927\u5c0f\u5927\u81f4\u76f8\u7b49\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2.<\/strong>\u9009\u62e9\u5176\u4e2d\u4e00\u4e2a\u6298\u53e0\u4f5c\u4e3a\u7ea6\u675f\u88c5\u7f6e\u3002\u5c06\u6a21\u677f\u8c03\u6574\u5230\u5269\u4f59\u7684 k-1 \u6298\u53e0\u3002\u6839\u636e\u5f20\u7d27\u5c42\u4e2d\u7684\u89c2\u6d4b\u503c\u8ba1\u7b97 MSE \u68c0\u9a8c\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3.<\/strong>\u91cd\u590d\u6b64\u8fc7\u7a0b<em>k<\/em>\u6b21\uff0c\u6bcf\u6b21\u4f7f\u7528\u4e0d\u540c\u7684\u96c6\u5408\u4f5c\u4e3a\u6392\u9664\u96c6\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>4.<\/strong>\u5c06\u603b\u4f53\u6d4b\u8bd5 MSE \u8ba1\u7b97\u4e3a<em>k \u4e2a<\/em>\u6d4b\u8bd5 MSE \u7684\u5e73\u5747\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728 R \u4e2d\u6267\u884c k \u6298\u4ea4\u53c9\u9a8c\u8bc1\u7684\u6700\u7b80\u5355\u65b9\u6cd5\u662f\u4f7f\u7528 R \u4e2d<strong>\u63d2\u5165\u7b26\u53f7<\/strong>\u5e93\u4e2d\u7684<strong>trainControl()<\/strong>\u548c<strong>train()<\/strong>\u51fd\u6570\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>trainControl()<\/strong>\u51fd\u6570\u7528\u4e8e\u6307\u5b9a\u8bad\u7ec3\u53c2\u6570\uff08\u4f8b\u5982\u8981\u4f7f\u7528\u7684\u4ea4\u53c9\u9a8c\u8bc1\u7c7b\u578b\u3001\u8981\u4f7f\u7528\u7684\u6298\u53e0\u6570\u7b49\uff09\uff0c <strong>train()<\/strong>\u51fd\u6570\u7528\u4e8e\u5b9e\u9645\u5c06\u6a21\u578b\u62df\u5408\u5230\u6570\u636e\u3002 \u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528<strong>trainControl()<\/strong>\u548c<strong>train()<\/strong>\u51fd\u6570\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1a\u5982\u4f55\u5728 R \u4e2d\u4f7f\u7528 trainControl()<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u5728 R \u4e2d\u6709\u4ee5\u4e0b\u6570\u636e\u96c6\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#create data frame<\/span>\ndf &lt;- data.frame(y=c(6, 8, 12, 14, 14, 15, 17, 22, 24, 23),\n                 x1=c(2, 5, 4, 3, 4, 6, 7, 5, 8, 9),\n                 x2=c(14, 12, 12, 13, 7, 8, 7, 4, 6, 5))\n\n<span style=\"color: #008080;\">#view data frame\n<\/span>df\n\ny x1 x2\n6 2 14\n8 5 12\n12 4 12\n14 3 13\n14 4 7\n15 6 8\n17 7 7\n22 5 4\n24 8 6\n23 9 5\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u73b0\u5728\u5047\u8bbe\u6211\u4eec\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/r\u4e2d\u7684lm\u51fd\u6570\/\" target=\"_blank\" rel=\"noopener\">lm()<\/a>\u51fd\u6570\u5bf9\u8be5\u6570\u636e\u96c6\u62df\u5408<a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52-1\/\" target=\"_blank\" rel=\"noopener\">\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\uff0c\u4f7f\u7528<strong>x1<\/strong>\u548c<strong>x2<\/strong>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c <strong>y<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#fit multiple linear regression model to data<\/span>\nfit &lt;- lm(y ~ x1 + x2, data=df)\n\n<span style=\"color: #008080;\">#view model summary\n<\/span>summary(fit)\n\nCall:\nlm(formula = y ~ x1 + x2, data = df)\n\nResiduals:\n    Min 1Q Median 3Q Max \n-3.6650 -1.9228 -0.3684 1.2783 5.0208 \n\nCoefficients:\n            Estimate Std. Error t value Pr(&gt;|t|)  \n(Intercept) 21.2672 6.9927 3.041 0.0188 *\nx1 0.7803 0.6942 1.124 0.2981  \nx2 -1.1253 0.4251 -2.647 0.0331 *\n---\nSignificant. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n\nResidual standard error: 3.093 on 7 degrees of freedom\nMultiple R-squared: 0.801, Adjusted R-squared: 0.7441 \nF-statistic: 14.09 on 2 and 7 DF, p-value: 0.003516\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4f7f\u7528\u6a21\u578b\u8f93\u51fa\u4e2d\u7684\u7cfb\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u62df\u5408\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>y = 21.2672 + 0.7803*(x <sub>1<\/sub> ) \u2013 1.1253(x <sub>2<\/sub> )<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u4e86\u89e3\u8be5\u6a21\u578b\u5728\u672a\u89c1\u8fc7\u7684<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u89c2\u5bdf\/\" target=\"_blank\" rel=\"noopener\">\u89c2\u5bdf<\/a>\u4e0a\u7684\u8868\u73b0\u5982\u4f55\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528 k \u6298\u4ea4\u53c9\u9a8c\u8bc1\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u6f14\u793a\u5982\u4f55\u4f7f\u7528<strong>\u63d2\u5165<\/strong>\u7b26\u53f7\u5305\u7684<strong>trainControl()<\/strong>\u51fd\u6570\u6765\u6307\u5b9a\u4f7f\u7528 5 \u6b21\u6298\u53e0 ( <strong>number=5<\/strong> ) \u7684 k \u6b21\u4ea4\u53c9\u9a8c\u8bc1 ( <strong>method=&#8221;cv&#8221;<\/strong> )\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7136\u540e\u6211\u4eec\u5c06\u6b64<strong>trainControl()<\/strong>\u51fd\u6570\u4f20\u9012\u7ed9<strong>train()<\/strong>\u51fd\u6570\u4ee5\u5b9e\u9645\u6267\u884c k \u6298\u4ea4\u53c9\u9a8c\u8bc1\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;\">library<\/span> (caret)<\/span>\n\n#specify the cross-validation method<\/span>\nctrl &lt;- trainControl(method = \" <span style=\"color: #ff0000;\">cv<\/span> \", number = <span style=\"color: #008000;\">5<\/span> )\n\n<span style=\"color: #008080;\">#fit a regression model and use k-fold CV to evaluate performance\n<\/span>model &lt;- train(y ~ x1 + x2, data = df, method = \" <span style=\"color: #ff0000;\">lm<\/span> \", trControl = ctrl)\n\n<span style=\"color: #008080;\">#view summary of k-fold CV               \n<\/span><span style=\"color: #008000;\">print<\/span> (model)\n\nLinear Regression \n\n10 samples\n 2 predictors\n\nNo pre-processing\nResampling: Cross-Validated (5 fold) \nSummary of sample sizes: 8, 8, 8, 8, 8 \nResampling results:\n\n  RMSE Rsquared MAE     \n  3.612302 1 3.232153\n\nTuning parameter 'intercept' was held constant at a value of TRUE\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6a21\u578b\u62df\u5408\u4e86<strong>5<\/strong>\u6b21\uff0c\u6bcf\u6b21\u4f7f\u7528<strong>8 \u4e2a<\/strong>\u89c2\u6d4b\u503c\u7684\u6837\u672c\u91cf\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6bcf\u6b21\uff0c\u8be5\u6a21\u578b\u90fd\u7528\u4e8e\u9884\u6d4b<strong>2 \u4e2a<\/strong>\u4fdd\u7559\u89c2\u6d4b\u503c\u7684\u503c\uff0c\u5e76\u4e14\u6bcf\u6b21\u90fd\u4f1a\u8ba1\u7b97\u4ee5\u4e0b\u6307\u6807\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>RMSE\uff1a<\/strong>\u5747\u65b9\u6839\u8bef\u5dee\u3002\u5b83\u8861\u91cf\u6a21\u578b\u9884\u6d4b\u4e0e\u5b9e\u9645\u89c2\u5bdf\u7ed3\u679c\u4e4b\u95f4\u7684\u5e73\u5747\u5dee\u5f02\u3002 RMSE \u8d8a\u4f4e\uff0c\u6a21\u578b\u9884\u6d4b\u5b9e\u9645\u89c2\u6d4b\u503c\u5c31\u8d8a\u51c6\u786e\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>MAE\uff1a<\/strong>\u5e73\u5747\u7edd\u5bf9\u8bef\u5dee\u3002\u8fd9\u662f\u6a21\u578b\u9884\u6d4b\u4e0e\u5b9e\u9645\u89c2\u6d4b\u503c\u4e4b\u95f4\u7684\u5e73\u5747\u7edd\u5bf9\u5dee\u5f02\u3002 MAE \u8d8a\u4f4e\uff0c\u6a21\u578b\u9884\u6d4b\u5b9e\u9645\u89c2\u6d4b\u503c\u5c31\u8d8a\u51c6\u786e\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7ed3\u679c\u4e2d\u663e\u793a\u4e94\u4e2a\u5206\u91cf\u7684 RMSE \u548c MAE \u503c\u7684\u5e73\u5747\u503c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5747\u65b9\u6839\u8bef\u5dee\uff1a <strong>3.612302<\/strong><\/span><\/li>\n<li><span style=\"color: #000000;\">\u6885\uff1a <strong>3.232153<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u8fd9\u4e9b\u6307\u6807\u8ba9\u6211\u4eec\u4e86\u89e3\u6a21\u578b\u5728\u65b0\u6570\u636e\u4e0a\u7684\u8868\u73b0\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u4f1a\u62df\u5408\u51e0\u4e2a\u4e0d\u540c\u7684\u6a21\u578b\u5e76\u6bd4\u8f83\u8fd9\u4e9b\u6307\u6807\uff0c\u4ee5\u786e\u5b9a\u54ea\u4e2a\u6a21\u578b\u5728\u672a\u89c1\u8fc7\u7684\u6570\u636e\u4e0a\u8868\u73b0\u6700\u597d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u6211\u4eec\u53ef\u4ee5\u62df\u5408<a href=\"https:\/\/statorials.org\/cn\/\u4f55\u65f6\u4f7f\u7528\u591a\u9879\u5f0f\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener\">\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b<\/a>\u5e76\u5bf9\u5176\u6267\u884c K \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u4ee5\u67e5\u770b RMSE \u548c MAE \u6307\u6807\u4e0e\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\u7684\u6bd4\u8f83\u60c5\u51b5\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f#1\uff1a<\/strong>\u5728\u6b64\u793a\u4f8b\u4e2d\uff0c\u6211\u4eec\u9009\u62e9\u4f7f\u7528 k=5 \u6298\u53e0\uff0c\u4f46\u60a8\u53ef\u4ee5\u9009\u62e9\u6240\u9700\u7684\u4efb\u610f\u6298\u53e0\u6b21\u6570\u3002\u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u901a\u5e38\u9009\u62e9 5 \u5230 10 \u5c42\u4e4b\u95f4\uff0c\u56e0\u4e3a\u4e8b\u5b9e\u8bc1\u660e\u8fd9\u662f\u4ea7\u751f\u53ef\u9760\u6d4b\u8bd5\u9519\u8bef\u7387\u7684\u6700\u4f73\u5c42\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f#2<\/strong> \uff1a <strong>trainControl()<\/strong>\u51fd\u6570\u63a5\u53d7\u8bb8\u591a\u6f5c\u5728\u7684\u53c2\u6570\u3002\u60a8\u53ef\u4ee5<a href=\"https:\/\/search.r-project.org\/CRAN\/refmans\/caret\/html\/trainControl.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230\u6b64\u529f\u80fd\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u63d0\u4f9b\u6709\u5173\u8bad\u7ec3\u6a21\u578b\u7684\u5176\u4ed6\u4fe1\u606f\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/cn\/k\u6298\u4ea4\u53c9\u9a8c\u8bc1\/\" target=\"_blank\" rel=\"noopener\">K \u6298\u4ea4\u53c9\u9a8c\u8bc1\u7b80\u4ecb<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u7559\u4e0b\u4e00\u4e2a\u4ea4\u53c9\u9a8c\u8bc1\/\" target=\"_blank\" rel=\"noopener\">\u7559\u4e00\u6cd5\u4ea4\u53c9\u9a8c\u8bc1\u7b80\u4ecb<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u673a\u5668\u5b66\u4e60\u8fc7\u5ea6\u62df\u5408\/\" target=\"_blank\" rel=\"noopener\">\u673a\u5668\u5b66\u4e60\u4e2d\u4ec0\u4e48\u662f\u8fc7\u5ea6\u62df\u5408\uff1f<\/a><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u4e3a\u4e86\u8bc4\u4f30\u6a21\u578b\u5bf9\u6570\u636e\u96c6\u7684\u62df\u5408\u7a0b\u5ea6\uff0c\u6211\u4eec\u9700\u8981\u5206\u6790\u5176\u5728\u4ee5\u524d\u4ece\u672a\u89c1\u8fc7\u7684\u89c2\u5bdf\u7ed3\u679c\u4e0a\u7684\u8868\u73b0\u3002 \u5b9e\u73b0\u6b64\u76ee\u7684\u7684\u6700\u5e38\u89c1\u65b9\u6cd5\u4e4b\u4e00\u662f [&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-4508","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\/ 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