{"id":518,"date":"2023-07-29T15:29:02","date_gmt":"2023-07-29T15:29:02","guid":{"rendered":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%95%e3%82%a9%e3%83%bc%e3%83%9e%e3%83%b3%e3%82%b9%e3%81%ae%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc%e3%82%92%e5%ae%9f\/"},"modified":"2023-07-29T15:29:02","modified_gmt":"2023-07-29T15:29:02","slug":"r-%e3%81%a6%e3%82%99%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%95%e3%82%a9%e3%83%bc%e3%83%9e%e3%83%b3%e3%82%b9%e3%81%ae%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc%e3%82%92%e5%ae%9f","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/r-%e3%81%a6%e3%82%99%e3%83%a2%e3%83%86%e3%82%99%e3%83%ab%e3%81%ae%e3%83%8f%e3%82%9a%e3%83%95%e3%82%a9%e3%83%bc%e3%83%9e%e3%83%b3%e3%82%b9%e3%81%ae%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc%e3%82%92%e5%ae%9f\/","title":{"rendered":"R \u3067\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u76f8\u4e92\u691c\u8a3c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u7d71\u8a08\u3067\u306f\u3001\u6b21\u306e 2 \u3064\u306e\u7406\u7531\u304b\u3089\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u3053\u3068\u304c\u3088\u304f\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> 1 \u3064\u4ee5\u4e0a\u306e\u4e88\u6e2c\u5909\u6570\u3068<span style=\"color: #000000;\">\u5fdc\u7b54\u5909\u6570<\/span><span style=\"color: #000000;\">\u306e\u9593\u306e\u95a2\u4fc2\u3092\u7406\u89e3\u3057\u307e\u3059<\/span>\u3002<\/li>\n<li><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u5c06\u6765\u306e\u89b3\u6e2c\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>\u76f8\u4e92\u691c\u8a3c\u306f\u3001<\/strong>\u30e2\u30c7\u30eb\u304c\u5c06\u6765\u306e\u89b3\u6e2c\u3092\u3069\u306e\u7a0b\u5ea6\u6b63\u78ba\u306b\u4e88\u6e2c\u3067\u304d\u308b\u304b\u3092\u63a8\u5b9a\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070<a href=\"https:\/\/statorials.org\/ja\/-10\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u3001<\/a><\/span><span style=\"color: #000000;\"><em>\u5e74\u9f62<\/em>\u3068<em>\u53ce\u5165\u3092<\/em>\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001<em>\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u30b9\u30c6\u30fc\u30bf\u30b9\u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066<\/em>\u4f7f\u7528\u3059\u308b\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3067\u304d\u307e\u3059\u3002<\/span><span style=\"color: #000000;\">\u3053\u306e\u5834\u5408\u3001\u30e2\u30c7\u30eb\u3092\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u3001\u305d\u306e\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u65b0\u898f\u7533\u8fbc\u8005\u306e\u53ce\u5165\u3068\u5e74\u9f62\u306b\u57fa\u3065\u3044\u3066<\/span><span style=\"color: #000000;\">\u3001\u30ed\u30fc\u30f3\u3092\u6ede\u7d0d\u3059\u308b\u53ef\u80fd\u6027\u3092\u4e88\u6e2c\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306b\u5f37\u529b\u306a\u4e88\u6e2c\u80fd\u529b\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\u306b\u306f\u3001\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u308c\u307e\u3067\u306b\u898b\u305f\u3053\u3068\u306e\u306a\u3044\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3046\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059<\/span><span style=\"color: #000000;\">\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30e2\u30c7\u30eb\u306e<strong>\u4e88\u6e2c\u8aa4\u5dee<\/strong>\u3092\u63a8\u5b9a\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<h2><strong><span style=\"color: #000000;\">\u76f8\u4e92\u691c\u8a3c\u3092\u4f7f\u7528\u3057\u305f\u4e88\u6e2c\u8aa4\u5dee\u306e\u63a8\u5b9a<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\"><strong>\u76f8\u4e92\u691c\u8a3c\u306f\u3001<\/strong>\u4e88\u6e2c\u8aa4\u5dee\u3092\u63a8\u5b9a\u3067\u304d\u308b\u3055\u307e\u3056\u307e\u306a\u65b9\u6cd5\u3092\u6307\u3057\u307e\u3059\u3002<\/span><span style=\"color: #000000;\">\u76f8\u4e92\u691c\u8a3c<\/span><span style=\"color: #000000;\">\u306e\u4e00\u822c\u7684\u306a\u30a2\u30d7\u30ed\u30fc\u30c1\u306f\u6b21<\/span>\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u7279\u5b9a\u306e\u6570\u306e\u89b3\u6e2c\u5024 (\u901a\u5e38\u306f\u3059\u3079\u3066\u306e\u89b3\u6e2c\u5024\u306e 15 \uff5e 25%) \u3092\u78ba\u4fdd\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>2.<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u4fdd\u5b58\u3057\u305f\u89b3\u6e2c\u5024\u306b\u30e2\u30c7\u30eb\u3092\u9069\u5408 (\u307e\u305f\u306f\u300c\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u300d) \u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>3.<\/strong>\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u4f7f\u7528\u3057\u306a\u304b\u3063\u305f\u89b3\u6e2c\u5024\u306b\u3064\u3044\u3066\u30e2\u30c7\u30eb\u304c\u3069\u306e\u7a0b\u5ea6\u6b63\u78ba\u306b\u4e88\u6e2c\u3067\u304d\u308b\u304b\u3092\u30c6\u30b9\u30c8\u3057\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u6e2c\u5b9a\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u9069\u5408\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u65b0\u3057\u3044\u89b3\u6e2c\u5024\u306b\u3064\u3044\u3066\u306e\u4e88\u6e2c\u3092\u884c\u3046\u5834\u5408\u3001\u6b21\u306e\u3088\u3046\u306a\u3044\u304f\u3064\u304b\u306e\u7570\u306a\u308b\u6307\u6a19\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u6e2c\u5b9a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u591a\u91cd R \u4e8c\u4e57:<\/strong>\u3053\u308c\u306f\u3001\u4e88\u6e2c\u5909\u6570\u3068\u5fdc\u7b54\u5909\u6570\u306e\u9593\u306e\u7dda\u5f62\u95a2\u4fc2\u306e\u5f37\u3055\u3092\u6e2c\u5b9a\u3057\u307e\u3059<\/span><span style=\"color: #000000;\">\u3002 R \u4e8c\u4e57\u306e 1 \u306e\u500d\u6570\u306f\u5b8c\u5168\u306a\u7dda\u5f62\u95a2\u4fc2\u3092\u793a\u3057\u3001<\/span> <span style=\"color: #000000;\">R \u4e8c\u4e57\u306e 0 \u306e\u500d\u6570\u306f\u7dda\u5f62\u95a2\u4fc2\u304c\u306a\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002 R \u4e8c\u4e57\u500d\u6570\u304c\u5927\u304d\u3044\u307b\u3069\u3001\u4e88\u6e2c\u5909\u6570\u304c\u5fdc\u7b54\u5909\u6570\u3092\u4e88\u6e2c\u3059\u308b\u53ef\u80fd\u6027\u304c\u9ad8\u304f\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<p>\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee (RMSE):<span style=\"color: #000000;\">\u65b0\u3057\u3044\u89b3\u6e2c<\/span><span style=\"color: #000000;\"><strong>\u5024<\/strong>\u3092\u4e88\u6e2c\u3059\u308b\u3068\u304d\u306b\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u751f\u3058\u308b\u5e73\u5747\u4e88\u6e2c\u8aa4\u5dee\u3092\u6e2c\u5b9a\u3057\u307e\u3059<\/span>\u3002<span style=\"color: #000000;\">\u3053\u308c\u306f\u3001\u89b3\u6e2c\u5024\u306e\u771f\u306e\u5024\u3068\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u4e88\u6e2c\u3055\u308c\u305f\u5024\u306e\u9593\u306e\u5e73\u5747\u8ddd\u96e2\u3067\u3059\u3002<\/span> <span style=\"color: #000000;\">RMSE \u306e<\/span>\u5024\u304c<span style=\"color: #000000;\">\u4f4e\u3044\u307b\u3069<\/span>\u3001\u30e2\u30c7\u30eb\u306e\u9069\u5408\u6027\u304c\u9ad8\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002<\/p>\n<p><span style=\"color: #000000;\"><strong>\u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee (MAE):<\/strong>\u3053\u308c\u306f\u3001\u89b3\u6e2c\u5024\u306e\u771f\u306e\u5024\u3068\u30e2\u30c7\u30eb\u306b\u3088\u3063\u3066\u4e88\u6e2c\u3055\u308c\u305f\u5024\u306e\u9593\u306e\u5e73\u5747\u7d76\u5bfe\u5dee\u3067\u3059\u3002<\/span><span style=\"color: #000000;\">\u3053\u306e\u30e1\u30c8\u30ea\u30af\u30b9\u306f\u901a\u5e38\u3001RMSE \u3088\u308a\u3082\u5916\u308c\u5024\u306e\u5f71\u97ff\u3092\u53d7\u3051\u306b\u304f\u3044\u3067\u3059\u3002 MAE \u306e\u5024\u304c\u4f4e\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306e\u9069\u5408\u6027\u304c\u9ad8\u3044\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>R \u3067\u306e 4 \u3064\u306e\u7570\u306a\u308b\u76f8\u4e92\u691c\u8a3c\u624b\u6cd5\u306e\u5b9f\u88c5<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001R \u3067\u6b21\u306e\u76f8\u4e92\u691c\u8a3c\u624b\u6cd5\u3092\u5b9f\u88c5\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u691c\u8a3c\u30bb\u30c3\u30c8\u306e\u30a2\u30d7\u30ed\u30fc\u30c1<\/span><br \/><span style=\"color: #000000;\"><strong>2.<\/strong> k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c<\/span><br \/><span style=\"color: #000000;\"><strong>3.<\/strong>\u76f8\u4e92\u691c\u8a3c\u306f\u8107\u306b\u7f6e\u304d\u307e\u3059<\/span><br \/><span style=\"color: #000000;\"><strong>4.<\/strong> k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u306e\u7e70\u308a\u8fd4\u3057<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u3055\u307e\u3056\u307e\u306a\u624b\u6cd5\u306e\u4f7f\u7528\u65b9\u6cd5\u3092\u8aac\u660e\u3059\u308b\u305f\u3081\u306b\u3001 <em>mtcars<\/em>\u7d44\u307f\u8fbc\u307f R \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define dataset\n<\/span>data &lt;- mtcars[, c(\"mpg\", \"disp\", \"hp\", \"drat\")]\n\n<span style=\"color: #008080;\">#view first six rows of new data\n<\/span>head(data)\n\n# mpg disp hp drat\n#Mazda RX4 21.0 160 110 3.90\n#Mazda RX4 Wag 21.0 160 110 3.90\n#Datsun 710 22.8 108 93 3.85\n#Hornet 4 Drive 21.4 258 110 3.08\n#Hornet Sportabout 18.7 360 175 3.15\n#Valiant 18.1 225 105 2.76\n<\/strong><\/pre>\n<p> disp \u3001 hp \u3001 drat \u3092\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001 mpg<span style=\"color: #000000;\">\u3092\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066<\/span><span style=\"color: #000000;\"><em>\u4f7f\u7528<\/em><em>\u3057<\/em><em>\u3066<\/em><em>\u91cd<\/em>\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059<\/span>\u3002<\/p>\n<h2><strong><span style=\"color: #000000;\">\u691c\u8a3c\u30bb\u30c3\u30c8\u306e\u30a2\u30d7\u30ed\u30fc\u30c1<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\"><strong>\u691c\u8a3c\u30bb\u30c3\u30c8\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306f<\/strong>\u6b21\u306e\u3088\u3046\u306b\u6a5f\u80fd\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u30c7\u30fc\u30bf\u3092 2 \u3064\u306e\u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u307e\u3059\u30021 \u3064\u306e\u30bb\u30c3\u30c8\u306f\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 (\u3064\u307e\u308a\u3001\u30e2\u30c7\u30eb \u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u306e\u63a8\u5b9a) \u306b\u4f7f\u7528\u3055\u308c<\/span><span style=\"color: #000000;\">\u3001\u3082\u3046 1 \u3064\u306e\u30bb\u30c3\u30c8\u306f\u30e2\u30c7\u30eb\u306e\u30c6\u30b9\u30c8\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u4e00\u822c\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u306f<\/span><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u306e 70 \uff5e 80% \u3092\u30e9\u30f3\u30c0\u30e0\u306b\u9078\u629e\u3057\u3066\u751f\u6210\u3055\u308c\u3001\u30c7\u30fc\u30bf\u306e\u6b8b\u308a\u306e 20 \uff5e 30% \u304c\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2.<\/strong>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>3.<\/strong>\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8 \u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u306e\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>4.<\/strong> R \u4e8c\u4e57\u3001RMSE\u3001MAE \u306a\u3069\u306e\u6307\u6a19\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u6e2c\u5b9a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><strong><span style=\"color: #000000;\">\u4f8b\uff1a<\/span><\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u3067\u306f\u3001\u4e0a\u3067\u5b9a\u7fa9\u3057\u305f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u307e\u305a\u3001\u30c7\u30fc\u30bf\u3092\u6b21\u306e\u3088\u3046\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/span><br \/><span style=\"color: #000000;\">\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3002\u30c7\u30fc\u30bf\u306e 80% \u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u3001\u30c7\u30fc\u30bf\u306e\u6b8b\u308a\u306e 20% \u3092<\/span><span style=\"color: #000000;\">\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3057\u307e\u3059\u3002\u6b21\u306b\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059<\/span><span style=\"color: #000000;\">\u3002\u6b21\u306b\u3001\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u306b\u95a2\u3059\u308b\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002<\/span>\u6700\u5f8c\u306b\u3001 <span style=\"color: #000000;\">R \u4e8c\u4e57\u3001RMSE\u3001MAE \u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb<\/span><span style=\"color: #000000;\">\u306e\u54c1\u8cea\u3092\u6e2c\u5b9a\u3057\u307e\u3059<\/span>\u3002<\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>dplyr<\/em> library used for data manipulation\n<\/span>library(dplyr)\n\n<span style=\"color: #008080;\">#load <em>caret<\/em> library used for partitioning data into training and test set\n<\/span>library(caret)\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(0)\n\n<span style=\"color: #008080;\">#define the dataset\n<\/span>data &lt;- mtcars[, c(\"mpg\", \"disp\", \"hp\", \"drat\")]\n\n<span style=\"color: #008080;\">#split the dataset into a training set (80%) and test set (20%).\n<\/span>training_obs &lt;- data$mpg %&gt;% createDataPartition(p = 0.8, list = FALSE)\n\ntrain &lt;- data[training_obs, ]\ntest &lt;- data[-training_obs, ]\n\n<span style=\"color: #008080;\"># Build the linear regression model on the training set\n<\/span>model &lt;- lm(mpg ~ ., data = train)\n\n<span style=\"color: #008080;\"># Use the model to make predictions on the test set\n<\/span>predictions &lt;- model %&gt;% predict(test)\n\n<span style=\"color: #008080;\">#Examine R-squared, RMSE, and MAE of predictions\n<\/span>data.frame(R_squared = R2(predictions, test$mpg),\n           RMSE = RMSE(predictions, test$mpg),\n           MAE = MAE(predictions, test$mpg))\n\n#R_squared RMSE MAE\n#1 0.9213066 1.876038 1.66614\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7570\u306a\u308b\u30e2\u30c7\u30eb\u3092\u6bd4\u8f03\u3059\u308b\u5834\u5408\u3001\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3067\u6700\u3082\u4f4e\u3044 RMSE \u3092\u751f\u6210\u3059\u308b\u30e2\u30c7\u30eb\u304c\u512a\u5148\u30e2\u30c7\u30eb\u3068\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u9577\u6240\u3068\u77ed\u6240<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u691c\u8a3c\u30bb\u30c3\u30c8\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u5229\u70b9\u306f\u3001\u30b7\u30f3\u30d7\u30eb\u3067\u8a08\u7b97\u52b9\u7387\u304c\u9ad8\u3044\u3053\u3068\u3067\u3059\u3002\u6b20\u70b9\u306f<\/span><span style=\"color: #000000;\">\u3001\u30e2\u30c7\u30eb\u304c\u5168\u30c7\u30fc\u30bf\u306e\u4e00\u90e8\u306e\u307f\u3092\u4f7f\u7528\u3057\u3066\u69cb\u7bc9\u3055\u308c\u308b\u3053\u3068\u3067\u3059\u3002<\/span>\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30bb\u30c3\u30c8\u304b\u3089<span style=\"color: #000000;\">\u9664\u5916\u3057\u305f\u30c7\u30fc\u30bf\u306b<\/span><span style=\"color: #000000;\">\u8208\u5473\u6df1\u3044\u60c5\u5831\u3084\u8cb4\u91cd\u306a\u60c5\u5831\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u5834\u5408\u3001\u30e2\u30c7\u30eb\u306f\u305d\u308c\u3092\u8003\u616e\u3057\u307e\u305b\u3093\u3002<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u30a2\u30d7\u30ed\u30fc\u30c1<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\"><strong>k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u30a2\u30d7\u30ed\u30fc\u30c1\u306f<\/strong>\u6b21\u306e\u3088\u3046\u306b\u6a5f\u80fd\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u30c7\u30fc\u30bf\u3092 k \u500b\u306e\u300c\u5206\u5272\u300d\u307e\u305f\u306f\u30b5\u30d6\u30bb\u30c3\u30c8 (\u305f\u3068\u3048\u3070\u30015 \u307e\u305f\u306f 10 \u500b\u306e\u30b5\u30d6\u30bb\u30c3\u30c8) \u306b\u30e9\u30f3\u30c0\u30e0\u306b\u5206\u5272\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>2.<\/strong> 1 \u3064\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u306e\u307f\u3092\u9664\u3044\u3066\u3001\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u3067\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>3.<\/strong>\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u9664\u5916\u3055\u308c\u305f\u30b5\u30d6\u30bb\u30c3\u30c8\u306e\u30c7\u30fc\u30bf\u306b\u3064\u3044\u3066\u4e88\u6e2c\u3092\u884c\u3044\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>4.<\/strong> k \u500b\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u306e\u305d\u308c\u305e\u308c\u304c\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3055\u308c\u308b\u307e\u3067\u3001\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u7e70\u308a\u8fd4\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>\uff15<\/strong> \uff0e k \u500b\u306e\u30c6\u30b9\u30c8\u30a8\u30e9\u30fc\u3092\u5e73\u5747\u3059\u308b\u3053\u3068\u3067\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u6e2c\u5b9a\u3057\u307e\u3059\u3002\u3053\u308c\u306f\u77e5\u3089\u308c\u3066\u3044\u307e\u3059<\/span><br \/><span style=\"color: #000000;\">\u76f8\u4e92\u691c\u8a3c\u30a8\u30e9\u30fc\u3068\u3057\u3066\u3002<\/span><\/p>\n<h3><strong><span style=\"color: #000000;\">\u4f8b<\/span><\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001\u307e\u305a\u30c7\u30fc\u30bf\u3092<\/span><span style=\"color: #000000;\">5 \u3064\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u306b\u5206\u5272\u3057\u307e\u3059\u3002\u6b21\u306b\u3001\u30c7\u30fc\u30bf\u306e\u30b5\u30d6\u30bb\u30c3\u30c8\u3092\u9664\u304f\u3059\u3079\u3066\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u307e\u3059\u3002\u6b21\u306b\u3001\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001<\/span><span style=\"color: #000000;\">\u9664\u5916\u3055\u308c\u305f\u30b5\u30d6\u30bb\u30c3\u30c8\u306b\u3064\u3044\u3066\u4e88\u6e2c\u3057\u3001\u30c6\u30b9\u30c8\u8aa4\u5dee\u3092\u8a18\u9332\u3057\u307e\u3059 (R \u4e8c\u4e57\u3001RMSE\u3001MAE \u3092\u4f7f\u7528)\u3002<\/span><span style=\"color: #000000;\">\u5404\u30b5\u30d6\u30bb\u30c3\u30c8\u304c\u30c6\u30b9\u30c8 \u30bb\u30c3\u30c8\u3068\u3057\u3066\u4f7f\u7528\u3055\u308c\u308b\u307e\u3067\u3001\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u7e70\u308a\u8fd4\u3057\u307e\u3059<\/span><span style=\"color: #000000;\">\u3002<\/span><span style=\"color: #000000;\">\u6b21\u306b\u30015 \u3064\u306e\u30c6\u30b9\u30c8\u30a8\u30e9\u30fc\u306e\u5e73\u5747\u3092\u5358\u7d14\u306b\u8a08\u7b97\u3057\u307e\u3059<\/span><span style=\"color: #000000;\">\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>dplyr<\/em> library used for data manipulation\n<\/span>library(dplyr)\n\n<span style=\"color: #008080;\">#load <em>caret<\/em> library used for partitioning data into training and test set\n<\/span>library(caret)\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(0)\n\n<span style=\"color: #008080;\">#define the dataset\n<\/span>data &lt;- mtcars[, c(\"mpg\", \"disp\", \"hp\", \"drat\")]\n\n<span style=\"color: #008080;\">#define the number of subsets (or \"folds\") to use\n<\/span>train_control &lt;- trainControl(method = \"cv\", number = 5)\n\n<span style=\"color: #008080;\">#train the model\n<\/span>model &lt;- train(mpg ~ ., data = data, method = \"lm\", trControl = train_control)\n\n<span style=\"color: #008080;\">#Summarize the results\n<\/span>print(model)\n\n#Linear Regression \n#\n#32 samples\n#3 predictor\n#\n#No pre-processing\n#Resampling: Cross-Validated (5 fold) \n#Summary of sample sizes: 26, 25, 26, 25, 26 \n#Resampling results:\n#\n# RMSE Rsquared MAE     \n#3.095501 0.7661981 2.467427\n#\n#Tuning parameter 'intercept' was held constant at a value of TRUE\n<\/strong><\/pre>\n<h3><strong><span style=\"color: #000000;\">\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u9577\u6240\u3068\u77ed\u6240<\/span><\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u691c\u8a3c\u30bb\u30c3\u30c8\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u5bfe\u3059\u308b k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u5229\u70b9\u306f\u3001\u6bce\u56de\u7570\u306a\u308b\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u8907\u6570\u56de\u69cb\u7bc9\u3059\u308b\u305f\u3081<\/span>\u3001<span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e<\/span><span style=\"color: #000000;\">\u69cb\u7bc9\u6642\u306b\u91cd\u8981\u306a\u30c7\u30fc\u30bf\u3092\u7701\u7565\u3059\u308b\u53ef\u80fd\u6027\u304c\u306a\u304f\u306a\u308b\u3053\u3068<\/span>\u3067\u3059\u3002<\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u4e3b\u89b3\u7684\u306a\u90e8\u5206\u306f\u3001k \u306b\u4f7f\u7528\u3059\u308b\u5024\u3001\u3064\u307e\u308a<\/span><span style=\"color: #000000;\">\u30c7\u30fc\u30bf\u3092\u5206\u5272\u3059\u308b\u30b5\u30d6\u30bb\u30c3\u30c8\u306e\u6570\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u4e00\u822c\u306b\u3001k \u5024\u304c\u4f4e\u3044\u307b\u3069\u30d0\u30a4\u30a2\u30b9\u306f\u9ad8\u304f\u306a\u308a\u307e\u3059\u304c\u3001\u5909\u52d5\u306f\u4f4e\u304f\u306a\u308a\u307e\u3059\u3002\u4e00\u65b9\u3001k \u5024\u304c\u9ad8\u3044\u3068\u3001<\/span><span style=\"color: #000000;\">\u30d0\u30a4\u30a2\u30b9\u306f\u4f4e\u304f\u306a\u308a\u307e\u3059\u304c\u3001\u5909\u52d5\u306f\u9ad8\u304f\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<p>\u5b9f\u969b\u306b\u306f\u3001\u3053\u306e<span style=\"color: #000000;\">\u30b5\u30d6\u30bb\u30c3\u30c8\u6570\u306b\u3088\u308a\u904e\u5ea6\u306e\u504f\u308a\u3068\u904e\u5ea6\u306e\u5909\u52d5\u3092\u540c\u6642\u306b\u56de\u907f\u3059\u308b\u50be\u5411\u304c\u3042\u308b<\/span><span style=\"color: #000000;\">\u305f\u3081\u3001\u4e00\u822c\u306b k \u306f 5 \u307e\u305f\u306f 10 \u306b\u7b49\u3057\u304f\u306a\u308b\u3088\u3046\u306b\u9078\u629e\u3055\u308c\u307e\u3059<\/span>\u3002<\/p>\n<h2> <span style=\"color: #000000;\"><strong>Leave One Out \u76f8\u4e92\u691c\u8a3c (LOOCV) \u30a2\u30d7\u30ed\u30fc\u30c1<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\"><strong>LOOCV \u30a2\u30d7\u30ed\u30fc\u30c1\u306f<\/strong>\u6b21\u306e\u3088\u3046\u306b\u6a5f\u80fd\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e 1 \u3064\u3092\u9664\u304f\u3059\u3079\u3066\u306e\u89b3\u6e2c\u5024\u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>2.<\/strong>\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u6b20\u843d\u3057\u3066\u3044\u308b\u89b3\u6e2c\u5024\u306e\u5024\u3092\u4e88\u6e2c\u3057\u307e\u3059\u3002\u3053\u306e\u4e88\u6e2c\u3092\u30c6\u30b9\u30c8\u3057\u305f\u969b\u306e\u30a8\u30e9\u30fc\u3092\u8a18\u9332\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>3.<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u89b3\u6e2c\u3054\u3068\u306b\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u7e70\u308a\u8fd4\u3057\u307e\u3059\u3002<\/span><br \/> <span style=\"color: #000000;\"><strong>4.<\/strong>\u3059\u3079\u3066\u306e\u4e88\u6e2c\u8aa4\u5dee\u3092\u5e73\u5747\u3057\u3066\u30e2\u30c7\u30eb\u306e\u54c1\u8cea\u3092\u6e2c\u5b9a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><strong><span style=\"color: #000000;\">\u4f8b<\/span><\/strong><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u524d\u306e\u4f8b\u3067\u4f7f\u7528\u3057\u305f\u306e\u3068\u540c\u3058\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3057\u3066 LOOCV \u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>dplyr<\/em> library used for data manipulation\n<\/span>library(dplyr)\n\n<span style=\"color: #008080;\">#load <em>caret<\/em> library used for partitioning data into training and test set\n<\/span>library(caret)\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(0)\n\n<span style=\"color: #008080;\">#define the dataset\n<\/span>data &lt;- mtcars[, c(\"mpg\", \"disp\", \"hp\", \"drat\")]\n\n<span style=\"color: #008080;\">#specify that we want to use LOOCV\n<\/span>train_control &lt;- trainControl( <span style=\"color: #800080;\">method = \"LOOCV\"<\/span> )\n\n<span style=\"color: #008080;\">#train the model\n<\/span>model &lt;- train(mpg ~ ., data = data, method = \"lm\", trControl = train_control)\n\n<span style=\"color: #008080;\">#summarize the results\n<\/span>print(model)\n\n#Linear Regression \n#\n#32 samples\n#3 predictor\n#\n#No pre-processing\n#Resampling: Leave-One-Out Cross-Validation \n#Summary of sample sizes: 31, 31, 31, 31, 31, 31, ... \n#Resampling results:\n#\n# RMSE Rsquared MAE     \n#3.168763 0.7170704 2.503544\n#\n#Tuning parameter 'intercept' was held constant at a value of TRUE\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u9577\u6240\u3068\u77ed\u6240<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">LOOCV \u306e\u5229\u70b9\u306f\u3001\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf \u30dd\u30a4\u30f3\u30c8\u3092\u4f7f\u7528\u3059\u308b\u305f\u3081\u3001\u4e00\u822c\u306b\u6f5c\u5728\u7684\u306a\u30d0\u30a4\u30a2\u30b9\u304c\u8efd\u6e1b\u3055\u308c\u308b\u3053\u3068\u3067\u3059\u3002\u305f\u3060\u3057\u3001<\/span><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u5404\u89b3\u6e2c\u5024\u306e\u5024\u3092\u4e88\u6e2c\u3059\u308b\u305f\u3081\u3001<\/span><span style=\"color: #000000;\">\u4e88\u6e2c\u8aa4\u5dee\u306e\u5909\u52d5\u304c\u5927\u304d\u304f\u306a\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u3082\u3046 1 \u3064\u306e\u6b20\u70b9\u306f\u3001\u975e\u5e38\u306b\u591a\u304f\u306e\u30e2\u30c7\u30eb\u306b\u9069\u5408\u3055\u305b\u308b\u5fc5\u8981\u304c\u3042\u308b\u305f\u3081\u3001\u975e\u52b9\u7387\u7684\u3067\u8a08\u7b97\u91cf\u304c\u591a\u304f\u306a\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<h2> <strong><span style=\"color: #000000;\">k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u7e70\u308a\u8fd4\u3059<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\"><strong>k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u8907\u6570\u56de\u5b9f\u884c\u3059\u308b\u3060\u3051\u3067\u3001k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u7e70\u308a\u8fd4\u3057<\/strong>\u5b9f\u884c\u3067\u304d\u307e\u3059\u3002\u6700\u7d42\u8aa4\u5dee\u306f\u3001<\/span><span style=\"color: #000000;\">\u7e70\u308a\u8fd4\u3057\u56de\u6570\u306e\u5e73\u5747\u8aa4\u5dee\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u3067\u306f\u30015 \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092 4 \u56de\u7e70\u308a\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>dplyr<\/em> library used for data manipulation\n<\/span>library(dplyr)\n\n<span style=\"color: #008080;\">#load <em>caret<\/em> library used for partitioning data into training and test set\n<\/span>library(caret)\n\n<span style=\"color: #008080;\">#make this example reproducible\n<\/span>set.seed(0)\n\n<span style=\"color: #008080;\">#define the dataset\n<\/span>data &lt;- mtcars[, c(\"mpg\", \"disp\", \"hp\", \"drat\")]\n\n<span style=\"color: #008080;\">#define the number of subsets to use and number of times to repeat k-fold CV\n<\/span>train_control &lt;- trainControl(method = \"repeatedcv\", number = 5, <span style=\"color: #800080;\">repeats = 4<\/span> )\n\n<span style=\"color: #008080;\">#train the model\n<\/span>model &lt;- train(mpg ~ ., data = data, method = \"lm\", trControl = train_control)\n\n<span style=\"color: #008080;\">#summarize the results\n<\/span>print(model)\n\n#Linear Regression \n#\n#32 samples\n#3 predictor\n#\n#No pre-processing\n#Resampling: Cross-Validated (5 fold, repeated 4 times) \n#Summary of sample sizes: 26, 25, 26, 25, 26, 25, ... \n#Resampling results:\n#\n# RMSE Rsquared MAE     \n#3.176339 0.7909337 2.559131\n#\n#Tuning parameter 'intercept' was held constant at a value of TRUE\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u9577\u6240\u3068\u77ed\u6240<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u7e70\u308a\u8fd4\u3059\u5229\u70b9\u306f\u3001\u7e70\u308a\u8fd4\u3057\u3054\u3068\u306b\u30c7\u30fc\u30bf\u304c\u308f\u305a\u304b\u306b\u7570\u306a\u308b\u30b5\u30d6\u30bb\u30c3\u30c8\u306b\u5206\u5272\u3055\u308c\u308b\u305f\u3081\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u8aa4\u5dee\u306e\u3055\u3089\u306b\u516c\u5e73\u306a\u63a8\u5b9a\u5024\u304c\u5f97\u3089\u308c\u308b\u3053\u3068\u3067\u3059\u3002\u3053\u306e\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u6b20\u70b9\u306f\u3001\u30e2\u30c7\u30eb \u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0 \u30d7\u30ed\u30bb\u30b9\u3092\u6570\u56de\u7e70\u308a\u8fd4\u3059\u5fc5\u8981\u304c\u3042\u308b\u305f\u3081\u3001\u8a08\u7b97\u91cf\u304c\u591a\u304f\u306a\u308b\u53ef\u80fd\u6027\u304c\u3042\u308b\u3053\u3068\u3067\u3059\u3002<\/span><\/p>\n<h2><strong><span style=\"color: #000000;\">\u76f8\u4e92\u691c\u8a3c\u3067\u30d5\u30a9\u30fc\u30eb\u30c9\u6570\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u76f8\u4e92\u691c\u8a3c\u306e\u6700\u3082\u4e3b\u89b3\u7684\u306a\u90e8\u5206\u306f\u3001\u4f7f\u7528\u3059\u308b\u30d5\u30a9\u30fc\u30eb\u30c9 (\u3064\u307e\u308a\u30b5\u30d6\u30bb\u30c3\u30c8) \u306e\u6570\u3092\u6c7a\u5b9a\u3059\u308b\u3053\u3068\u3067\u3059\u3002\u4e00\u822c\u306b\u3001\u30d5\u30a9\u30fc\u30eb\u30c9\u306e\u6570\u304c\u5c11\u306a\u3044\u307b\u3069\u3001\u8aa4\u5dee\u63a8\u5b9a\u5024\u306e\u504f\u308a\u306f\u5927\u304d\u304f\u306a\u308a\u307e\u3059\u304c\u3001\u8aa4\u5dee\u306f\u5c0f\u3055\u304f\u306a\u308a\u307e\u3059\u3002\u9006\u306b\u3001\u30d5\u30a9\u30fc\u30eb\u30c9\u6570\u304c\u591a\u3044\u307b\u3069\u3001\u8aa4\u5dee\u63a8\u5b9a\u306e\u504f\u308a\u306f\u5c11\u306a\u304f\u306a\u308a\u307e\u3059\u304c\u3001\u3070\u3089\u3064\u304d\u304c\u5927\u304d\u304f\u306a\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8a08\u7b97\u6642\u9593\u306b\u7559\u610f\u3059\u308b\u3053\u3068\u3082\u91cd\u8981\u3067\u3059\u3002\u6298\u308a\u76ee\u3054\u3068\u306b\u65b0\u3057\u3044\u30d1\u30bf\u30fc\u30f3\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306f\u6642\u9593\u306e\u304b\u304b\u308b\u30d7\u30ed\u30bb\u30b9\u3067\u3059\u304c\u3001\u6298\u308a\u76ee\u306e\u6570\u3092\u591a\u304f\u9078\u629e\u3059\u308b\u3068\u3001\u6642\u9593\u304c\u304b\u304b\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5b9f\u969b\u306b\u306f\u3001\u76f8\u4e92\u691c\u8a3c\u306f\u901a\u5e38 5 \u307e\u305f\u306f 10 \u56de\u306e\u5206\u5272\u3067\u5b9f\u884c\u3055\u308c\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u5909\u52d5\u6027\u3068\u504f\u308a\u306e\u30d0\u30e9\u30f3\u30b9\u304c\u53d6\u308c\u3001\u8a08\u7b97\u52b9\u7387\u3082\u5411\u4e0a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h2><strong>\u76f8\u4e92\u691c\u8a3c\u3092\u5b9f\u884c\u3057\u305f\u5f8c\u306b\u30e2\u30c7\u30eb\u3092\u9078\u629e\u3059\u308b\u65b9\u6cd5<\/strong><\/h2>\n<p><span style=\"color: #000000;\">\u76f8\u4e92\u691c\u8a3c\u306f\u3001\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u8aa4\u5dee\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002\u3053\u308c\u306f\u3001(RMSE\u3001R \u4e8c\u4e57\u306a\u3069\u306b\u57fa\u3065\u3044\u3066) \u4e88\u6e2c\u8aa4\u5dee\u304c\u6700\u3082\u4f4e\u3044\u30e2\u30c7\u30eb\u3092\u5f37\u8abf\u8868\u793a\u3059\u308b\u3053\u3068\u3067\u30012 \u3064\u4ee5\u4e0a\u306e\u7570\u306a\u308b\u30e2\u30c7\u30eb\u304b\u3089\u9078\u629e\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u76f8\u4e92\u691c\u8a3c\u3092\u4f7f\u7528\u3057\u3066\u6700\u9069\u306a\u30e2\u30c7\u30eb\u3092\u9078\u629e\u3057\u305f\u3089\u3001\u5229\u7528\u53ef\u80fd\u306a<em>\u3059\u3079\u3066\u306e<\/em>\u30c7\u30fc\u30bf\u3092\u4f7f\u7528\u3057\u3066\u3001\u9078\u629e\u3057\u305f\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u307e\u3059\u3002\u6700\u7d42\u30e2\u30c7\u30eb\u306e\u76f8\u4e92\u691c\u8a3c\u4e2d\u306b\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3057\u305f\u5b9f\u969b\u306e\u30e2\u30c7\u30eb \u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306f\u4f7f\u7528\u3057\u307e\u305b\u3093\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u30015 \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092\u4f7f\u7528\u3057\u3066\u30012 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