{"id":1178,"date":"2023-07-27T09:44:39","date_gmt":"2023-07-27T09:44:39","guid":{"rendered":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84k%e6%8a%98%e4%ba%a4%e5%8f%89%e9%aa%8c%e8%af%81\/"},"modified":"2023-07-27T09:44:39","modified_gmt":"2023-07-27T09:44:39","slug":"r%e4%b8%ad%e7%9a%84k%e6%8a%98%e4%ba%a4%e5%8f%89%e9%aa%8c%e8%af%81","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/r%e4%b8%ad%e7%9a%84k%e6%8a%98%e4%ba%a4%e5%8f%89%e9%aa%8c%e8%af%81\/","title":{"rendered":"R \u4e2d\u7684 k \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff08\u9010\u6b65\uff09"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u4e3a\u4e86\u8bc4\u4f30\u6a21\u578b\u5728\u6570\u636e\u96c6\u4e0a\u7684\u6027\u80fd\uff0c\u6211\u4eec\u9700\u8981\u8861\u91cf\u6a21\u578b\u505a\u51fa\u7684\u9884\u6d4b\u4e0e\u89c2\u5bdf\u5230\u7684\u6570\u636e\u7684\u5339\u914d\u7a0b\u5ea6\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6267\u884c\u6b64\u64cd\u4f5c\u7684\u5e38\u7528\u65b9\u6cd5\u79f0\u4e3a<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<a href=\"https:\/\/www.rdocumentation.org\/packages\/caret\/versions\/6.0-86\/topics\/trainControl\" target=\"_blank\" rel=\"noopener noreferrer\">trainControl()<\/a>\u51fd\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u672c\u6559\u7a0b\u63d0\u4f9b\u4e86\u4e00\u4e2a\u5feb\u901f\u793a\u4f8b\uff0c\u8bf4\u660e\u5982\u4f55\u4f7f\u7528\u6b64\u51fd\u6570\u5bf9 R \u4e2d\u7684\u7ed9\u5b9a\u6a21\u578b\u6267\u884c k \u6298\u4ea4\u53c9\u9a8c\u8bc1\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1aR \u4e2d\u7684 K \u91cd\u4ea4\u53c9\u9a8c\u8bc1<\/strong><\/span><\/h3>\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;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u5728 R \u4e2d\u5c06 <a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b<\/a>\u62df\u5408\u5230\u8be5\u6570\u636e\u96c6\uff0c\u5e76\u6267\u884c k = 5 \u6b21\u7684 k \u500d\u4ea4\u53c9\u9a8c\u8bc1\u6765\u8bc4\u4f30\u6a21\u578b\u7684\u6027\u80fd\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\"><span style=\"color: #993300;\">library<\/span> (caret)<\/span>\n\n#specify the cross-validation method<\/span>\nctrl &lt;- trainControl(method = \" <span style=\"color: #008000;\">cv<\/span> \", number = 5)\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: #008000;\">lm<\/span> \", trControl = ctrl)\n\n<span style=\"color: #008080;\">#view summary of k-fold CV               \n<\/span>print(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  3.018979 1 2.882348\n\nTuning parameter 'intercept' was held constant at a value of TRUE\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u662f\u5982\u4f55\u89e3\u91ca\u7ed3\u679c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u6ca1\u6709\u8fdb\u884c\u4efb\u4f55\u9884\u5904\u7406\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u5728\u62df\u5408\u6a21\u578b\u4e4b\u524d\u6211\u4eec\u6ca1\u6709\u4ee5\u4efb\u4f55\u65b9\u5f0f<a href=\"https:\/\/statorials.org\/cn\/\u5982\u4f55\u89c4\u8303\u5316r\u4e2d\u7684\u6570\u636e\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7f29\u653e\u6570\u636e<\/a>\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6211\u4eec\u7528\u4e8e\u8bc4\u4f30\u6a21\u578b\u7684\u91cd\u91c7\u6837\u65b9\u6cd5\u662f 5 \u500d\u4ea4\u53c9\u9a8c\u8bc1\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6bcf\u4e2a\u8bad\u7ec3\u96c6\u7684\u6837\u672c\u91cf\u4e3a 8\u3002<\/span><\/li>\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>Rsquared\uff1a<\/strong>\u8fd9\u662f\u6a21\u578b\u9884\u6d4b\u4e0e\u5b9e\u9645\u89c2\u5bdf\u7ed3\u679c\u4e4b\u95f4\u76f8\u5173\u6027\u7684\u5ea6\u91cf\u3002 R \u5e73\u65b9\u8d8a\u9ad8\uff0c\u6a21\u578b\u9884\u6d4b\u5b9e\u9645\u89c2\u6d4b\u503c\u7684\u51c6\u786e\u6027\u5c31\u8d8a\u9ad8\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\u63d0\u4f9b\u7684\u4e09\u4e2a\u6d4b\u91cf\u503c\uff08RMSE\u3001R \u5e73\u65b9\u548c MAE\uff09\u4e2d\u7684\u6bcf\u4e00\u4e2a\u90fd\u8ba9\u6211\u4eec\u4e86\u89e3\u6a21\u578b\u5728\u672a\u53d1\u5e03\u6570\u636e\u4e0a\u7684\u6027\u80fd\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\uff0c\u5e76\u6bd4\u8f83\u6b64\u5904\u63d0\u4f9b\u7684\u7ed3\u679c\u63d0\u4f9b\u7684\u4e09\u4e2a\u6307\u6807\uff0c\u4ee5\u786e\u5b9a\u54ea\u4e2a\u6a21\u578b\u4ea7\u751f\u6700\u4f4e\u7684\u6d4b\u8bd5\u9519\u8bef\u7387\uff0c\u56e0\u6b64\u662f\u6700\u597d\u4f7f\u7528\u7684\u6a21\u578b\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u68c0\u67e5\u6700\u7ec8\u7684\u6a21\u578b\u62df\u5408\u60c5\u51b5\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view final model<\/span>\nmodel$finalModel\n\nCall:\nlm(formula = .outcome ~ ., data = dat)\n\nCoefficients:\n(Intercept) x1 x2  \n    21.2672 0.7803 -1.1253  \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u7ec8\u6a21\u578b\u5982\u4e0b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>y = 21.2672 + 0.7803*(x <sub>1<\/sub> ) \u2013 1.12538(x <sub>2<\/sub> )<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u663e\u793a\u5bf9\u6bcf\u6b21\u6298\u53e0\u6240\u505a\u7684\u6a21\u578b\u9884\u6d4b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view predictions for each fold<\/span>\nmodel$resample\n\n      RMSE Rsquared MAE Resample\n1 4.808773 1 3.544494 Fold1\n2 3.464675 1 3.366812 Fold2\n3 6.281255 1 6.280702 Fold3\n4 3.759222 1 3.573883 Fold4\n5 1.741127 1 1.679767 Fold5<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u8bf7\u6ce8\u610f\uff0c\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","protected":false},"excerpt":{"rendered":"<p>\u4e3a\u4e86\u8bc4\u4f30\u6a21\u578b\u5728\u6570\u636e\u96c6\u4e0a\u7684\u6027\u80fd\uff0c\u6211\u4eec\u9700\u8981\u8861\u91cf\u6a21\u578b\u505a\u51fa\u7684\u9884\u6d4b\u4e0e\u89c2\u5bdf\u5230\u7684\u6570\u636e\u7684\u5339\u914d\u7a0b\u5ea6\u3002 \u6267\u884c\u6b64\u64cd\u4f5c\u7684\u5e38\u7528\u65b9\u6cd5\u79f0\u4e3ak [&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-1178","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>R \u4e2d\u7684 K \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff08\u9010\u6b65\uff09<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u4ecb\u7ecd\u5982\u4f55\u5728 R \u4e2d\u6267\u884c k \u6298\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u5305\u62ec\u5206\u6b65\u793a\u4f8b\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\/r\u4e2d\u7684k\u6298\u4ea4\u53c9\u9a8c\u8bc1\/\" \/>\n<meta property=\"og:locale\" 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