{"id":4502,"date":"2023-07-10T13:36:46","date_gmt":"2023-07-10T13:36:46","guid":{"rendered":"https:\/\/statorials.org\/ja\/%e3%83%88%e3%83%ac%e3%82%a4%e3%83%b3%e3%82%b3%e3%83%b3%e3%83%88%e3%83%ad%e3%83%bc%e3%83%abr\/"},"modified":"2023-07-10T13:36:46","modified_gmt":"2023-07-10T13:36:46","slug":"%e3%83%88%e3%83%ac%e3%82%a4%e3%83%b3%e3%82%b3%e3%83%b3%e3%83%88%e3%83%ad%e3%83%bc%e3%83%abr","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/%e3%83%88%e3%83%ac%e3%82%a4%e3%83%b3%e3%82%b3%e3%83%b3%e3%83%88%e3%83%ad%e3%83%bc%e3%83%abr\/","title":{"rendered":"A: traincontrol \u3092\u4f7f\u7528\u3057\u3066\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3092\u5236\u5fa1\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

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\u30e2\u30c7\u30eb\u304c\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u3069\u306e\u7a0b\u5ea6\u9069\u5408\u3067\u304d\u308b\u304b\u3092\u8a55\u4fa1\u3059\u308b\u306b\u306f\u3001\u3053\u308c\u307e\u3067\u306b\u898b\u305f\u3053\u3068\u306e\u306a\u3044\u89b3\u6e2c\u7d50\u679c\u3067\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5206\u6790\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n

\u3053\u308c\u3092\u5b9f\u73fe\u3059\u308b\u6700\u3082\u4e00\u822c\u7684\u306a\u65b9\u6cd5\u306e 1 \u3064\u306f\u3001\u6b21\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u4f7f\u7528\u3059\u308bk \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092<\/a>\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n

1.<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u307b\u307c\u540c\u3058\u30b5\u30a4\u30ba\u306ek<\/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

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

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\u3092k<\/em>\u56de\u7e70\u308a\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n

4.<\/strong> 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

R \u3067 k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092\u5b9f\u884c\u3059\u308b\u6700\u3082\u7c21\u5358\u306a\u65b9\u6cd5\u306f\u3001R \u306e\u30ad\u30e3\u30ec\u30c3\u30c8<\/strong>\u30e9\u30a4\u30d6\u30e9\u30ea\u306etrainControl()<\/strong>\u95a2\u6570\u3068train()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n

trainControl()<\/strong>\u95a2\u6570\u306f\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d1\u30e9\u30e1\u30fc\u30bf\u30fc (\u4f7f\u7528\u3059\u308b\u76f8\u4e92\u691c\u8a3c\u306e\u7a2e\u985e\u3001\u4f7f\u7528\u3059\u308b\u5206\u5272\u6570\u306a\u3069) \u3092\u6307\u5b9a\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u3001 train()<\/strong>\u95a2\u6570\u306f\u5b9f\u969b\u306b\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u306b\u9069\u5408\u3055\u305b\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002 \u3002<\/span><\/p>\n

\u6b21\u306e\u4f8b\u306f\u3001 trainControl()<\/strong>\u95a2\u6570\u3068train()<\/strong>\u95a2\u6570\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n

\u4f8b: R \u3067 trainControl() \u3092\u4f7f\u7528\u3059\u308b\u65b9\u6cd5<\/strong><\/span><\/h2>\n

R \u306b\u6b21\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u3042\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n

 #create data frame<\/span>\ndf <- 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#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
\u3053\u3053\u3067\u3001 lm()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001 x1<\/strong>\u3068x2<\/strong>\u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001 y<\/strong>\u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066\u3001\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092<\/a>\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u8fd1\u4f3c\u3059\u308b\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #fit multiple linear regression model to data<\/span>\nfit <- lm(y ~ x1 + x2, data=df)\n\n#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(>|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
\u30e2\u30c7\u30eb\u51fa\u529b\u306e\u4fc2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u8fd1\u4f3c\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
 y = 21.2672 + 0.7803*(x 1<\/sub> ) \u2013 1.1253(x 2<\/sub> )<\/strong><\/span><\/p>\n
\u3053\u306e\u30e2\u30c7\u30eb\u304c\u76ee\u306b\u898b\u3048\u306a\u3044\u89b3\u6e2c<\/a>\u306b\u5bfe\u3057\u3066\u3069\u306e\u7a0b\u5ea6\u3046\u307e\u304f\u6a5f\u80fd\u3059\u308b\u304b\u3092\u77e5\u308b\u305f\u3081\u306b\u3001k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u30ad\u30e3\u30ec\u30c3\u30c8<\/strong>\u30d1\u30c3\u30b1\u30fc\u30b8\u306etrainControl()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30015 \u5206\u5272 ( number=5<\/strong> ) \u3092\u4f7f\u7528\u3059\u308b k \u5206\u5272\u76f8\u4e92\u691c\u8a3c ( method=”cv”<\/strong> ) \u3092\u6307\u5b9a\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
\u6b21\u306b\u3001\u3053\u306etrainControl()<\/strong>\u95a2\u6570\u3092train()<\/strong>\u95a2\u6570\u306b\u6e21\u3057\u3066\u3001\u5b9f\u969b\u306b k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092\u5b9f\u884c\u3057\u307e\u3059\u3002<\/span><\/p>\n
 library<\/span> (caret)<\/span>\n\n#specify the cross-validation method<\/span>\nctrl <- trainControl(method = \" cv<\/span> \", number = 5<\/span> )\n\n#fit a regression model and use k-fold CV to evaluate performance\n<\/span>model <- train(y ~ x1 + x2, data = df, method = \" lm<\/span> \", trControl = ctrl)\n\n#view summary of k-fold CV               \n<\/span>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
\u7d50\u679c\u304b\u3089\u3001\u6bce\u56de8 \u3064\u306e<\/strong>\u89b3\u6e2c\u5024\u306e\u30b5\u30f3\u30d7\u30eb \u30b5\u30a4\u30ba\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u304c5<\/strong>\u56de\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3055\u308c\u305f\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
\u6bce\u56de\u3001\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u30662 \u3064<\/strong>\u306e\u4fdd\u6301\u3055\u308c\u305f\u89b3\u6e2c\u5024\u306e\u4e88\u6e2c\u304c\u884c\u308f\u308c\u3001\u6bce\u56de\u6b21\u306e\u6307\u6a19\u304c\u8a08\u7b97\u3055\u308c\u307e\u3057\u305f\u3002<\/span><\/p>\n