{"id":1198,"date":"2023-07-27T07:49:23","date_gmt":"2023-07-27T07:49:23","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%a6%e3%82%99%e3%81%ae%e3%81%aa%e3%81%91%e3%82%99%e3%81%aa%e3%82%8f%e5%9b%9e%e5%b8%b0\/"},"modified":"2023-07-27T07:49:23","modified_gmt":"2023-07-27T07:49:23","slug":"python%e3%81%a6%e3%82%99%e3%81%ae%e3%81%aa%e3%81%91%e3%82%99%e3%81%aa%e3%82%8f%e5%9b%9e%e5%b8%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%a6%e3%82%99%e3%81%ae%e3%81%aa%e3%81%91%e3%82%99%e3%81%aa%e3%82%8f%e5%9b%9e%e5%b8%b0\/","title":{"rendered":"Python \u3067\u306e\u306a\u3052\u306a\u308f\u56de\u5e30 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ja\/\u306a\u3051\u3099\u306a\u308f\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u30e9\u30c3\u30bd\u56de\u5e30\u306f<\/a>\u3001\u30c7\u30fc\u30bf\u306b<a href=\"https:\/\/statorials.org\/ja\/\u591a\u91cd\u5171\u7dda\u6027\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u591a\u91cd\u5171\u7dda\u6027\u304c<\/a>\u5b58\u5728\u3059\u308b\u5834\u5408\u306b\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7c21\u5358\u306b\u8a00\u3046\u3068\u3001\u6700\u5c0f\u4e8c\u4e57\u56de\u5e30\u306f\u3001\u6b8b\u5dee\u4e8c\u4e57\u548c (RSS) \u3092\u6700\u5c0f\u5316\u3059\u308b\u4fc2\u6570\u63a8\u5b9a\u5024\u3092\u898b\u3064\u3051\u3088\u3046\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS = \u03a3(y <sub>i<\/sub> \u2013 \u0177 <sub>i<\/sub> )2<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u03a3<\/strong> : \u548c\u3092\u610f\u5473\u3059\u308b\u30ae\u30ea\u30b7\u30e3\u8a9e\u306e<em>\u8a18\u53f7<\/em><\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> : <sup>i \u756a\u76ee\u306e<\/sup>\u89b3\u6e2c\u5024\u306e\u5b9f\u969b\u306e\u5fdc\u7b54\u5024<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> : \u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u4e88\u6e2c\u5fdc\u7b54\u5024<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u9006\u306b\u3001\u306a\u3052\u306a\u308f\u56de\u5e30\u3067\u306f\u3001\u4ee5\u4e0b\u3092\u6700\u5c0f\u9650\u306b\u6291\u3048\u3088\u3046\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>RSS + \u03bb\u03a3|\u03b2 <sub>j<\/sub> |<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u3053\u3067\u3001 <em>j<\/em>\u306f 1 \u304b\u3089<em>p \u500b\u306e<\/em>\u4e88\u6e2c\u5b50\u5909\u6570\u3067\u3042\u308a\u3001\u03bb \u2265 0 \u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u65b9\u7a0b\u5f0f\u306e\u3053\u306e 2 \u756a\u76ee\u306e\u9805\u306f\u3001<em>\u64a4\u9000\u30da\u30ca\u30eb\u30c6\u30a3<\/em>\u3068\u3057\u3066\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002\u30e9\u30c3\u30bd\u56de\u5e30\u3067\u306f\u3001\u53ef\u80fd\u306a\u9650\u308a\u6700\u5c0f\u306e MSE (\u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee) \u30c6\u30b9\u30c8\u3092\u751f\u6210\u3059\u308b \u03bb \u306e\u5024\u3092\u9078\u629e\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u306a\u3052\u306a\u308f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306e\u6bb5\u968e\u7684\u306a\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 1: \u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001Python \u3067\u306a\u3052\u306a\u308f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">from<\/span> numpy <span style=\"color: #008000;\">import<\/span> arange\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LassoCV\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 2: \u30c7\u30fc\u30bf\u3092\u30ed\u30fc\u30c9\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u300133 \u53f0\u306e\u7570\u306a\u308b\u8eca\u306b\u95a2\u3059\u308b\u60c5\u5831\u304c\u542b\u307e\u308c\u308b<strong>mtcars<\/strong>\u3068\u3044\u3046\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066<strong>hp \u3092<\/strong>\u4f7f\u7528\u3057\u3001\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u6b21\u306e\u5909\u6570\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg<\/span><\/li>\n<li><span style=\"color: #000000;\">\u91cd\u3055<\/span><\/li>\n<li><span style=\"color: #000000;\">\u305f\u308f\u3054\u3068<\/span><\/li>\n<li><span style=\"color: #000000;\">qsec<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30ed\u30fc\u30c9\u3057\u3066\u8868\u793a\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located<\/span>\nurl = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data<\/span>\ndata_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"wt\", \"drat\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data<\/span>\ndata[0:6]\n\n\tmpg wt drat qsec hp\n0 21.0 2.620 3.90 16.46 110\n1 21.0 2.875 3.90 17.02 110\n2 22.8 2.320 3.85 18.61 93\n3 21.4 3.215 3.08 19.44 110\n4 18.7 3,440 3.15 17.02 175\n5 18.1 3.460 2.76 20.22 105<\/strong><\/span><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 3: \u306a\u3052\u306a\u308f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001sklearn \u306e<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.RidgeCV.html\" target=\"_blank\" rel=\"noopener noreferrer\">LassoCV()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u30e9\u30c3\u30bd\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3057\u3001 <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RepeatedKFold.html\" target=\"_blank\" rel=\"noopener noreferrer\">RepeatedKFold()<\/a>\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066 k \u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u5b9f\u884c\u3057\u3001\u30da\u30ca\u30eb\u30c6\u30a3\u9805\u306b\u4f7f\u7528\u3059\u308b\u6700\u9069\u306a\u30a2\u30eb\u30d5\u30a1\u5024\u3092\u898b\u3064\u3051\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><em><strong>\u6ce8:<\/strong> Python \u3067\u306f\u300c\u30e9\u30e0\u30c0\u300d\u306e\u4ee3\u308f\u308a\u306b\u300c\u30a2\u30eb\u30d5\u30a1\u300d\u3068\u3044\u3046\u7528\u8a9e\u304c\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/em><\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u306f\u3001k = 10 \u5206\u5272\u3092\u9078\u629e\u3057\u3001\u76f8\u4e92\u691c\u8a3c\u30d7\u30ed\u30bb\u30b9\u3092 3 \u56de\u7e70\u308a\u8fd4\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305f\u3001LassoCV() \u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u3067\u30a2\u30eb\u30d5\u30a1\u5024 0\u30011\u30011\u300110 \u306e\u307f\u3092\u30c6\u30b9\u30c8\u3059\u308b\u3053\u3068\u306b\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002\u305f\u3060\u3057\u3001\u72ec\u81ea\u306e\u30a2\u30eb\u30d5\u30a1\u7bc4\u56f2\u3092 0 \u304b\u3089 1 \u307e\u3067 0.01 \u523b\u307f\u3067\u8a2d\u5b9a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]]\ny = data[\"hp\"]\n\n<span style=\"color: #008080;\">#define cross-validation method to evaluate model\n<\/span>cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#define model\n<\/span>model = LassoCV(alphas= <span style=\"color: #3366ff;\">arange<\/span> (0, 1, 0.01), cv=cv, n_jobs=<\/strong><\/span> <span style=\"color: #008000;\"><strong>-1<\/strong><\/span> <span style=\"color: #000000;\"><strong>)\n\n<span style=\"color: #008080;\">#fit model\n<\/span>model. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n\n<span style=\"color: #008080;\">#display lambda that produced the lowest test MSE\n<\/span>print( <span style=\"color: #3366ff;\">model.alpha_<\/span> )\n\n0.99<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u30c6\u30b9\u30c8\u306e MSE \u3092\u6700\u5c0f\u5316\u3059\u308b\u30e9\u30e0\u30c0\u5024\u306f<strong>0.99<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u30b9\u30c6\u30c3\u30d7 4: \u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u4e88\u6e2c\u3092\u884c\u3046<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u5f8c\u306b\u3001\u6700\u5f8c\u306e\u306a\u3052\u306a\u308f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u65b0\u3057\u3044\u89b3\u6e2c\u5024\u306b\u3064\u3044\u3066\u306e\u4e88\u6e2c\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u305f\u3068\u3048\u3070\u3001\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u6b21\u306e\u5c5e\u6027\u3092\u6301\u3064\u65b0\u3057\u3044\u8eca\u3092\u5b9a\u7fa9\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">mpg: 24<\/span><\/li>\n<li><span style=\"color: #000000;\">\u91cd\u91cf: 2.5<\/span><\/li>\n<li><span style=\"color: #000000;\">\u4fa1\u683c: 3.5<\/span><\/li>\n<li> <span style=\"color: #000000;\">qsec: 18.5<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u9069\u5408\u3057\u305f\u306a\u3052\u306a\u308f\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u3001\u3053\u306e\u65b0\u3057\u3044\u89b3\u6e2c\u5024\u306e<em>hp<\/em>\u5024\u3092\u4e88\u6e2c\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define new observation\n<span style=\"color: #000000;\">new = [24, 2.5, 3.5, 18.5]\n<\/span>\n#predict hp value using lasso regression model\n<span style=\"color: #000000;\">model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n<\/span>\n<span style=\"color: #000000;\">array([105.63442071])\n<\/span><\/span><\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u5165\u529b\u3055\u308c\u305f\u5024\u306b\u57fa\u3065\u3044\u3066\u3001\u30e2\u30c7\u30eb\u306f\u3053\u306e\u8eca\u306e<em>\u99ac\u529b<\/em>\u5024\u304c<strong>105.63442071<\/strong>\u306b\u306a\u308b\u3068\u4e88\u6e2c\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u4f8b\u3067\u4f7f\u7528\u3055\u308c\u3066\u3044\u308b\u5b8c\u5168\u306a Python \u30b3\u30fc\u30c9\u306f\u3001 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/lasso_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u3053\u3053\u3067<\/a>\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u30e9\u30c3\u30bd\u56de\u5e30\u306f\u3001\u30c7\u30fc\u30bf\u306b\u591a\u91cd\u5171\u7dda\u6027\u304c\u5b58\u5728\u3059\u308b\u5834\u5408\u306b\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u308b\u65b9\u6cd5\u3067\u3059\u3002 \u7c21\u5358\u306b\u8a00\u3046\u3068\u3001\u6700\u5c0f\u4e8c\u4e57\u56de\u5e30\u306f\u3001\u6b8b\u5dee\u4e8c\u4e57\u548c (RSS) \u3092\u6700\u5c0f\u5316\u3059\u308b\u4fc2\u6570\u63a8\u5b9a\u5024\u3092\u898b\u3064\u3051\u3088\u3046\u3068\u3057\u307e\u3059\u3002 RSS = \u03a3(y i  [&hellip;]<\/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-1198","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python \u3067\u306e\u306a\u3052\u306a\u308f\u56de\u5e30 (\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7) - \u7d71\u8a08<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u306a\u3052\u306a\u308f\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u3092\u3001\u30b9\u30c6\u30c3\u30d7\u30d0\u30a4\u30b9\u30c6\u30c3\u30d7\u306e\u4f8b\u3092\u542b\u3081\u3066\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, 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