{"id":1175,"date":"2023-07-27T09:44:39","date_gmt":"2023-07-27T09:44:39","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae-k-%e5%88%86%e5%89%b2%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc\/"},"modified":"2023-07-27T09:44:39","modified_gmt":"2023-07-27T09:44:39","slug":"r-%e3%81%a6%e3%82%99%e3%81%ae-k-%e5%88%86%e5%89%b2%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%81%ae-k-%e5%88%86%e5%89%b2%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc\/","title":{"rendered":"R \u3067\u306e k \u30d5\u30a9\u30fc\u30eb\u30c9\u76f8\u4e92\u691c\u8a3c (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3059\u308b\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3059\u308b\u306b\u306f\u3001\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u305f\u4e88\u6e2c\u304c\u89b3\u5bdf\u3055\u308c\u305f\u30c7\u30fc\u30bf\u3068\u3069\u306e\u7a0b\u5ea6\u4e00\u81f4\u3059\u308b\u304b\u3092\u6e2c\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3092\u884c\u3046\u305f\u3081\u306b\u4e00\u822c\u7684\u306b\u4f7f\u7528\u3055\u308c\u308b\u65b9\u6cd5\u306f<a href=\"https:\/\/statorials.org\/ja\/k-\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u3001 k \u5206\u5272\u76f8\u4e92\u691c\u8a3c<\/a>\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u304a\u308a\u3001\u6b21\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u307b\u307c\u540c\u3058\u30b5\u30a4\u30ba\u306e<em>k<\/em>\u30b0\u30eb\u30fc\u30d7\u3001\u3064\u307e\u308a\u300c\u5206\u5272\u300d\u306b\u30e9\u30f3\u30c0\u30e0\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2.<\/strong>\u3072\u3060\u306e 1 \u3064\u3092\u62d8\u675f\u30bb\u30c3\u30c8\u3068\u3057\u3066\u9078\u629e\u3057\u307e\u3059\u3002\u30c6\u30f3\u30d7\u30ec\u30fc\u30c8\u3092\u6b8b\u308a\u306e k-1 \u500b\u306e\u6298\u308a\u76ee\u306b\u5408\u308f\u305b\u3066\u8abf\u6574\u3057\u307e\u3059\u3002\u5f35\u529b\u304c\u304b\u304b\u3063\u305f\u5c64\u306e\u89b3\u5bdf\u7d50\u679c\u306b\u57fa\u3065\u3044\u3066 MSE \u30c6\u30b9\u30c8\u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3.<\/strong>\u6bce\u56de\u7570\u306a\u308b\u30bb\u30c3\u30c8\u3092\u9664\u5916\u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3092<em>k<\/em>\u56de\u7e70\u308a\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>4.<\/strong> <em>k \u500b<\/em>\u306e\u30c6\u30b9\u30c8 MSE \u306e\u5e73\u5747\u3068\u3057\u3066\u5168\u4f53\u306e\u30c6\u30b9\u30c8 MSE \u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u3067 k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092\u5b9f\u884c\u3059\u308b\u6700\u3082\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001R \u306e<strong>\u30ad\u30e3\u30ec\u30c3\u30c8<\/strong>\u30e9\u30a4\u30d6\u30e9\u30ea\u306e<a href=\"https:\/\/www.rdocumentation.org\/packages\/caret\/versions\/6.0-86\/topics\/trainControl\" target=\"_blank\" rel=\"noopener noreferrer\">trainControl()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 R \u306e\u7279\u5b9a\u306e\u30e2\u30c7\u30eb\u306b\u5bfe\u3057\u3066 k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u7c21\u5358\u306a\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b: R \u3067\u306e K \u30d5\u30a9\u30fc\u30eb\u30c9\u76f8\u4e92\u691c\u8a3c<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">R \u306b\u6b21\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/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;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001<a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30r\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092<\/a>R \u306e\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u8fd1\u4f3c\u3057\u3001k = 5 \u56de\u3067 k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092\u5b9f\u884c\u3057\u3066\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/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;\">\u7d50\u679c\u3092\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u524d\u51e6\u7406\u306f\u884c\u308f\u308c\u307e\u305b\u3093\u3067\u3057\u305f\u3002\u3064\u307e\u308a\u3001\u30e2\u30c7\u30eb\u3092\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3059\u308b\u524d\u306b\u3001\u3044\u304b\u306a\u308b\u65b9\u6cd5\u3067\u3082<a href=\"https:\/\/statorials.org\/ja\/r\u306e\u30c6\u3099\u30fc\u30bf\u3092\u6b63\u898f\u5316\u3059\u308b\u65b9\u6cd5\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u30c7\u30fc\u30bf\u3092\u30b9\u30b1\u30fc\u30ea\u30f3\u30b0\u3057<\/a>\u307e\u305b\u3093\u3067\u3057\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3057\u305f\u30ea\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u65b9\u6cd5\u306f 5 \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3067\u3057\u305f\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5404\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u306e\u30b5\u30f3\u30d7\u30eb \u30b5\u30a4\u30ba\u306f 8 \u3067\u3057\u305f\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>RMSE:<\/strong>\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee\u3002\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u305f\u4e88\u6e2c\u3068\u5b9f\u969b\u306e\u89b3\u6e2c\u5024\u306e\u9593\u306e\u5e73\u5747\u5dee\u3092\u6e2c\u5b9a\u3057\u307e\u3059\u3002 RMSE \u304c\u4f4e\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u5b9f\u969b\u306e\u89b3\u6e2c\u5024\u3092\u3088\u308a\u6b63\u78ba\u306b\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>R2 \u4e57:<\/strong>\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u305f\u4e88\u6e2c\u3068\u5b9f\u969b\u306e\u89b3\u6e2c\u5024\u306e\u9593\u306e\u76f8\u95a2\u95a2\u4fc2\u306e\u5c3a\u5ea6\u3067\u3059\u3002 R \u4e8c\u4e57\u304c\u5927\u304d\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u5b9f\u969b\u306e\u89b3\u6e2c\u3092\u3088\u308a\u6b63\u78ba\u306b\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>MAE:<\/strong>\u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee\u3002\u3053\u308c\u306f\u3001\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u305f\u4e88\u6e2c\u3068\u5b9f\u969b\u306e\u89b3\u6e2c\u5024\u306e\u9593\u306e\u5e73\u5747\u7d76\u5bfe\u5dee\u3067\u3059\u3002 MAE \u304c\u4f4e\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u5b9f\u969b\u306e\u89b3\u6e2c\u3092\u3088\u308a\u6b63\u78ba\u306b\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u3067\u63d0\u4f9b\u3055\u308c\u308b 3 \u3064\u306e\u6e2c\u5b9a\u5024 (RMSE\u3001R \u4e8c\u4e57\u3001MAE) \u306e\u305d\u308c\u305e\u308c\u304b\u3089\u3001\u672a\u516c\u958b\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308b\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306e\u30a2\u30a4\u30c7\u30a2\u304c\u5f97\u3089\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b9f\u969b\u306b\u306f\u3001\u901a\u5e38\u3001\u3044\u304f\u3064\u304b\u306e\u7570\u306a\u308b\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u3001\u3053\u3053\u3067\u793a\u3057\u305f\u7d50\u679c\u306b\u3088\u3063\u3066\u63d0\u4f9b\u3055\u308c\u308b 3 \u3064\u306e\u6307\u6a19\u3092\u6bd4\u8f03\u3057\u3066\u3001\u3069\u306e\u30e2\u30c7\u30eb\u304c\u6700\u3082\u4f4e\u3044\u30c6\u30b9\u30c8\u30a8\u30e9\u30fc\u7387\u3092\u751f\u6210\u3059\u308b\u304b\u3001\u3057\u305f\u304c\u3063\u3066\u4f7f\u7528\u3059\u308b\u306e\u306b\u6700\u9069\u306a\u30e2\u30c7\u30eb\u3092\u6c7a\u5b9a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u6700\u7d42\u7684\u306a\u30e2\u30c7\u30eb\u306e\u9069\u5408\u6027\u3092\u8abf\u3079\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view final model<\/span>\nmodel$finalModel\n\nCall:\nlm(formula = .outcome ~ ., data = dat)\n\nCoefficients:\n(Intercept) x1 x2  \n    21.2672 0.7803 -1.1253  \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6700\u7d42\u7684\u306a\u30e2\u30c7\u30eb\u306f\u6b21\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>y = 21.2672 + 0.7803*(x <sub>1<\/sub> ) \u2013 1.12538(x <sub>2<\/sub> )<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3057\u3066\u3001\u5404\u30d5\u30a9\u30fc\u30eb\u30c9\u306b\u5bfe\u3057\u3066\u884c\u308f\u308c\u305f\u30e2\u30c7\u30eb\u4e88\u6e2c\u3092\u8868\u793a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#view predictions for each fold<\/span>\nmodel$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;\">\u3053\u306e\u4f8b\u3067\u306f\u3001k=5 \u306e\u6298\u308a\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3092\u9078\u629e\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u5fc5\u8981\u306a\u6298\u308a\u306e\u6570\u3092\u9078\u629e\u3067\u304d\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u5b9f\u969b\u306b\u306f\u3001\u4fe1\u983c\u6027\u306e\u9ad8\u3044\u30c6\u30b9\u30c8\u30a8\u30e9\u30fc\u7387\u3092\u751f\u307f\u51fa\u3059\u6700\u9069\u306a\u5c64\u6570\u3067\u3042\u308b\u3053\u3068\u304c\u8a3c\u660e\u3055\u308c\u3066\u3044\u308b\u305f\u3081\u3001\u901a\u5e38\u306f 5 \uff5e 10 \u5c64\u306e\u9593\u3067\u9078\u629e\u3057\u307e\u3059\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3059\u308b\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3059\u308b\u306b\u306f\u3001\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u884c\u308f\u308c\u305f\u4e88\u6e2c\u304c\u89b3\u5bdf\u3055\u308c\u305f\u30c7\u30fc\u30bf\u3068\u3069\u306e\u7a0b\u5ea6\u4e00\u81f4\u3059\u308b\u304b\u3092\u6e2c\u5b9a\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002 \u3053\u308c\u3092\u884c\u3046\u305f\u3081\u306b\u4e00\u822c\u7684\u306b\u4f7f\u7528\u3055\u308c\u308b\u65b9\u6cd5\u306f\u3001 k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3068\u3057\u3066\u77e5\u3089 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