{"id":1748,"date":"2023-07-25T03:30:46","date_gmt":"2023-07-25T03:30:46","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%aeaic\/"},"modified":"2023-07-25T03:30:46","modified_gmt":"2023-07-25T03:30:46","slug":"python%e3%81%aeaic","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%aeaic\/","title":{"rendered":"Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e aic \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u8d64\u6c60\u60c5\u5831\u91cf\u57fa\u6e96 (AIC) \u306f\u3001\u3055\u307e\u3056\u307e\u306a\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u9069\u5408\u5ea6\u3092\u6bd4\u8f03\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308b\u6307\u6a19\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">AIC = 2K \u2013 2 <em>ln<\/em> (\u9577\u3055)<\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>K:<\/strong>\u30e2\u30c7\u30eb\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u6570\u3002 K \u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306f 2 \u3067\u3042\u308b\u305f\u3081\u3001\u4e88\u6e2c\u5909\u6570\u304c 1 \u3064\u3060\u3051\u3042\u308b\u30e2\u30c7\u30eb\u306e K \u5024\u306f 2+1 = 3 \u306b\u306a\u308a\u307e\u3059\u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong><em>ln<\/em> (L)<\/strong> : \u30e2\u30c7\u30eb\u306e\u5bfe\u6570\u5c24\u5ea6\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u30c7\u30fc\u30bf\u304c\u4e0e\u3048\u3089\u308c\u305f\u5834\u5408\u306e\u30e2\u30c7\u30eb\u306e\u78ba\u7387\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">AIC \u306f\u3001\u904e\u5270\u306a\u6570\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u30fc\u3092\u4f7f\u7528\u3059\u308b\u30e2\u30c7\u30eb\u306b\u30da\u30ca\u30eb\u30c6\u30a3\u3092\u8ab2\u3057\u306a\u304c\u3089\u3001\u30c7\u30fc\u30bf\u306e\u5909\u52d5\u3092\u6700\u3082\u591a\u304f\u8aac\u660e\u3059\u308b\u30e2\u30c7\u30eb\u3092\u898b\u3064\u3051\u308b\u3088\u3046\u306b\u8a2d\u8a08\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<p>\u8907\u6570\u306e\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u305f\u3089\u3001<span style=\"color: #000000;\">\u5404\u30e2\u30c7\u30eb\u306e AIC \u5024<\/span><span style=\"color: #000000;\">\u3092\u6bd4\u8f03\u3067\u304d\u307e\u3059<\/span>\u3002 <span style=\"color: #000000;\">AIC \u304c\u6700\u3082\u4f4e\u3044\u30e2\u30c7\u30eb\u304c\u6700\u9069\u306a\u9069\u5408\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">Python \u3067\u91cd\u56de\u5e30\u30e2\u30c7\u30eb\u306e AIC \u3092\u8a08\u7b97\u3059\u308b\u306b\u306f\u3001 <strong>statsmodels.regression.linear_model.OLS()<\/strong>\u95a2\u6570\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u95a2\u6570\u306b\u306f\u3001\u7279\u5b9a\u306e\u30e2\u30c7\u30eb\u306e AIC \u5024\u3092\u793a\u3059<strong>aic<\/strong>\u3068\u3044\u3046\u30d7\u30ed\u30d1\u30c6\u30a3\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001Python \u306e\u3055\u307e\u3056\u307e\u306a\u56de\u5e30\u30e2\u30c7\u30eb\u306e AIC \u3092\u8a08\u7b97\u304a\u3088\u3073\u89e3\u91c8\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b: Python \u3067 AIC \u3092\u8a08\u7b97\u304a\u3088\u3073\u89e3\u91c8\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\"><strong>mtcars<\/strong>\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5909\u6570\u3092\u4f7f\u7528\u3057\u3066 2 \u3064\u306e\u7570\u306a\u308b<a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092<\/a>\u8fd1\u4f3c\u3057\u305f\u3044\u3068\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u307e\u305a\u3001\u3053\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LinearRegression\n<span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#define URL where dataset is located\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data\n<\/span>data = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#view head of data\n<\/span>data. <span style=\"color: #3366ff;\">head<\/span> ()\n\n        model mpg cyl disp hp drat wt qsec vs am gear carb\n0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4\n1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4\n2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1\n3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1\n4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2\n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u5404\u30e2\u30c7\u30eb\u3067\u4f7f\u7528\u3059\u308b\u4e88\u6e2c\u5909\u6570\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30e2\u30c7\u30eb 1 \u306e\u4e88\u6e2c\u5909\u6570: disp\u3001hp\u3001wt\u3001qsec<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30e2\u30c7\u30eb 2 \u306e\u4e88\u6e2c\u5b50\u5909\u6570: disp\u3001qsec<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u6700\u521d\u306e\u30e2\u30c7\u30eb\u3092\u9069\u5408\u3055\u305b\u3066 AIC \u3092\u8a08\u7b97\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 response variable\n<\/span>y = data['mpg']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = data[['disp', 'hp', 'wt', 'qsec']]\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view AIC of model\n<\/span><span style=\"color: #993300;\">print<\/span> (model. <span style=\"color: #3366ff;\">aic<\/span> )\n\n157.06960941462438<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30e2\u30c7\u30eb\u306e AIC \u306f<strong>157.07<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u30012 \u756a\u76ee\u306e\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u3066 AIC \u3092\u8a08\u7b97\u3057\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define response variable\n<\/span>y = data['mpg']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = data[['disp', 'qsec']]\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view AIC of model\n<\/span><span style=\"color: #993300;\">print<\/span> (model. <span style=\"color: #3366ff;\">aic<\/span> )\n\n169.84184864154588<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30e2\u30c7\u30eb\u306e AIC \u306f<strong>169.84<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6700\u521d\u306e\u30e2\u30c7\u30eb\u306f AIC \u5024\u304c\u4f4e\u3044\u305f\u3081\u3001\u6700\u3082\u3088\u304f\u9069\u5408\u3059\u308b\u30e2\u30c7\u30eb\u3067\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u3053\u306e\u30e2\u30c7\u30eb\u304c\u6700\u9069\u3067\u3042\u308b\u3068\u7279\u5b9a\u3057\u305f\u3089\u3001\u30e2\u30c7\u30eb\u306e\u30d5\u30a3\u30c3\u30c6\u30a3\u30f3\u30b0\u3092\u9032\u3081\u3001R \u4e8c\u4e57\u5024\u3084\u30d9\u30fc\u30bf\u4fc2\u6570\u3092\u542b\u3080\u7d50\u679c\u3092\u5206\u6790\u3057\u3066\u3001\u4e00\u9023\u306e\u4e88\u6e2c\u5909\u6570\u3068<a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener\">\u5fdc\u7b54\u5909\u6570<\/a>\u306e\u9593\u306e\u6b63\u78ba\u306a\u95a2\u4fc2\u3092\u6c7a\u5b9a\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h3>\n<p><a href=\"https:\/\/statorials.org\/ja\/\u7dda\u5f62\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener\">Python \u306e\u7dda\u5f62\u56de\u5e30\u306e\u5b8c\u5168\u30ac\u30a4\u30c9<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/python\u306er\u4e8c\u4e57\u306f\u8abf\u6574\u3057\u307e\u3059\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u8abf\u6574\u6e08\u307f R \u4e8c\u4e57\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u8d64\u6c60\u60c5\u5831\u91cf\u57fa\u6e96 (AIC) \u306f\u3001\u3055\u307e\u3056\u307e\u306a\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u9069\u5408\u5ea6\u3092\u6bd4\u8f03\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u308b\u6307\u6a19\u3067\u3059\u3002 \u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002 AIC = 2K \u2013 2 ln (\u9577\u3055) \u91d1\uff1a K:\u30e2\u30c7\u30eb\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u6570\u3002 K \u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306f [&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-1748","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\u56de\u5e30\u30e2\u30c7\u30eb\u306e AIC \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u8d64\u6c60\u60c5\u5831\u91cf\u57fa\u6e96 (AIC) \u5024\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u8aac\u660e\u3057\u307e\u3059\u3002\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/ja\/#website\"},\"datePublished\":\"2023-07-25T03:30:46+00:00\",\"dateModified\":\"2023-07-25T03:30:46+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/ja\/#\/schema\/person\/86b92d2dd87368b26360d19d9c6a5d83\"},\"description\":\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u8d64\u6c60\u60c5\u5831\u91cf\u57fa\u6e96 (AIC) 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