{"id":1176,"date":"2023-07-27T09:36:52","date_gmt":"2023-07-27T09:36:52","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%a6%e3%82%99%e3%81%aek%e3%83%95%e3%82%a9%e3%83%bc%e3%83%ab%e3%83%88%e3%82%99%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc\/"},"modified":"2023-07-27T09:36:52","modified_gmt":"2023-07-27T09:36:52","slug":"python%e3%81%a6%e3%82%99%e3%81%aek%e3%83%95%e3%82%a9%e3%83%bc%e3%83%ab%e3%83%88%e3%82%99%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%a6%e3%82%99%e3%81%aek%e3%83%95%e3%82%a9%e3%83%bc%e3%83%ab%e3%83%88%e3%82%99%e7%9b%b8%e4%ba%92%e6%a4%9c%e8%a8%bc\/","title":{"rendered":"Python \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>\n

\n

\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

\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<\/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

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

\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u7279\u5b9a\u306e\u30e2\u30c7\u30eb\u306b\u5bfe\u3057\u3066 k \u5206\u5272\u76f8\u4e92\u691c\u8a3c\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n

\u30b9\u30c6\u30c3\u30d7 1: \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30ed\u30fc\u30c9\u3059\u308b<\/strong><\/span><\/h3>\n

\u307e\u305a\u3001\u3053\u306e\u4f8b\u306b\u5fc5\u8981\u306a\u95a2\u6570\u3068\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30ed\u30fc\u30c9\u3057\u307e\u3059\u3002<\/span><\/p>\n

 from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> KFold\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> cross_val_score\nfrom<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\nfrom<\/span> numpy import<\/span> means\nfrom<\/span> numpy import<\/span> absolute\nfrom<\/span> numpy import<\/span> sqrt\nimport<\/span> pandas as<\/span> pd\n<\/strong><\/span><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 2: \u30c7\u30fc\u30bf\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306b\u30012 \u3064\u306e\u4e88\u6e2c\u5909\u6570x1<\/sub>\u3068x2<\/sub>\u3068 1 \u3064\u306e\u5fdc\u7b54\u5909\u6570 y \u3092\u542b\u3080 pandas DataFrame \u3092\u4f5c\u6210\u3057\u307e\u3059\u3002<\/span><\/p>\n
 df = pd.DataFrame({' y<\/span> ': [6, 8, 12, 14, 14, 15, 17, 22, 24, 23],\n                   ' x1<\/span> ': [2, 5, 4, 3, 4, 6, 7, 5, 8, 9],\n                   ' x2<\/span> ': [14, 12, 12, 13, 7, 8, 7, 4, 6, 5]})\n<\/strong><\/span><\/pre>\n
\u30b9\u30c6\u30c3\u30d7 3: K-Fold \u76f8\u4e92\u691c\u8a3c\u3092\u5b9f\u884c\u3059\u308b<\/strong><\/span><\/h3>\n
\u6b21\u306b\u3001 \u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092<\/a>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u9069\u5408\u3055\u305b\u3001LOOCV \u3092\u5b9f\u884c\u3057\u3066\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3057\u307e\u3059\u3002<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 10<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span>\n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use k-fold CV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_absolute_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view mean absolute error\n<\/span>mean(absolute(scores))\n\n3.6141267491803646\n<\/strong><\/span><\/pre>\n
\u7d50\u679c\u304b\u3089\u3001\u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee (MAE) \u304c3.614<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002\u3064\u307e\u308a\u3001\u30e2\u30c7\u30eb\u4e88\u6e2c\u3068\u5b9f\u969b\u306b\u89b3\u6e2c\u3055\u308c\u305f\u30c7\u30fc\u30bf\u306e\u9593\u306e\u5e73\u5747\u7d76\u5bfe\u8aa4\u5dee\u306f 3.614 \u3067\u3059\u3002<\/span><\/p>\n
\u4e00\u822c\u306b\u3001MAE \u304c\u4f4e\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u5b9f\u969b\u306e\u89b3\u6e2c\u3092\u3088\u308a\u9069\u5207\u306b\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u306b\u3088\u304f\u4f7f\u7528\u3055\u308c\u308b\u3082\u3046 1 \u3064\u306e\u6307\u6a19\u306f\u3001\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee (RMSE) \u3067\u3059\u3002\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001LOOCV \u3092\u4f7f\u7528\u3057\u3066\u3053\u306e\u30e1\u30c8\u30ea\u30af\u30b9\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 5<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span> \n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use LOOCV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_squared_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view RMSE\n<\/span>sqrt(mean(absolute(scores)))\n\n4.284373111711816<\/strong><\/span><\/pre>\n
\u7d50\u679c\u304b\u3089\u3001\u4e8c\u4e57\u5e73\u5747\u5e73\u65b9\u6839\u8aa4\u5dee (RMSE) \u304c4.284<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n
 RMSE \u304c\u4f4e\u3044\u307b\u3069\u3001\u30e2\u30c7\u30eb\u306f\u5b9f\u969b\u306e\u89b3\u6e2c\u3092\u3088\u308a\u9069\u5207\u306b\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n
\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\u5404\u30e2\u30c7\u30eb\u306e RMSE \u307e\u305f\u306f MAE \u3092\u6bd4\u8f03\u3057\u3066\u3001\u30c6\u30b9\u30c8 \u30a8\u30e9\u30fc\u7387\u304c\u6700\u3082\u4f4e\u3044\u30e2\u30c7\u30eb\u3001\u3064\u307e\u308a\u4f7f\u7528\u3059\u308b\u306e\u306b\u6700\u9069\u306a\u30e2\u30c7\u30eb\u3092\u6c7a\u5b9a\u3057\u307e\u3059\u3002<\/span><\/p>\n
\u307e\u305f\u3001\u3053\u306e\u4f8b\u3067\u306f k=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\u3082\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/span><\/p>\n
\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
\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n
K-Fold \u76f8\u4e92\u691c\u8a3c\u306e\u6982\u8981<\/a>
Python \u306e\u7dda\u5f62\u56de\u5e30\u306e\u5b8c\u5168\u30ac\u30a4\u30c9<\/a>
Python \u3067\u306e Leave-One-Out \u76f8\u4e92\u691c\u8a3c<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-1176","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\nPython \u3067\u306e K \u30d5\u30a9\u30fc\u30eb\u30c9\u76f8\u4e92\u691c\u8a3c (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7) - 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