{"id":1201,"date":"2023-07-27T07:35:07","date_gmt":"2023-07-27T07:35:07","guid":{"rendered":"https:\/\/statorials.org\/ja\/python%e3%81%aer%e4%ba%8c%e4%b9%97%e3%81%af%e8%aa%bf%e6%95%b4%e3%81%97%e3%81%be%e3%81%99\/"},"modified":"2023-07-27T07:35:07","modified_gmt":"2023-07-27T07:35:07","slug":"python%e3%81%aer%e4%ba%8c%e4%b9%97%e3%81%af%e8%aa%bf%e6%95%b4%e3%81%97%e3%81%be%e3%81%99","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/python%e3%81%aer%e4%ba%8c%e4%b9%97%e3%81%af%e8%aa%bf%e6%95%b4%e3%81%97%e3%81%be%e3%81%99\/","title":{"rendered":"Python \u3067\u8abf\u6574\u6e08\u307f r \u4e8c\u4e57\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>R2 \u306f<\/strong>\u3001\u591a\u304f\u306e\u5834\u5408<sup>R2<\/sup>\u3068\u66f8\u304b\u308c\u3001<a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b<a href=\"https:\/\/statorials.org\/ja\/\u5909\u6570\u306e\u8aac\u660e\u5fdc\u7b54\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5fdc\u7b54\u5909\u6570<\/a>\u306e\u5206\u6563\u306e\u5272\u5408\u3067\u3059\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\">R \u4e8c\u4e57\u306e\u5024\u306e\u7bc4\u56f2\u306f 0 \u304b\u3089 1 \u3067\u3059\u3002\u5024 0 \u306f\u3001\u5fdc\u7b54\u5909\u6570\u304c\u4e88\u6e2c\u5909\u6570\u306b\u3088\u3063\u3066\u307e\u3063\u305f\u304f\u8aac\u660e\u3067\u304d\u306a\u3044\u3053\u3068\u3092\u793a\u3057\u3001\u5024 1 \u306f\u3001\u5fdc\u7b54\u5909\u6570\u304c\u4e88\u6e2c\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u3053\u3068\u3092\u793a\u3057\u307e\u3059\u3002\u4e88\u6e2c\u5b50\u306b\u3088\u3063\u3066\u30a8\u30e9\u30fc\u306a\u304f\u5b8c\u5168\u306b\u8aac\u660e\u3055\u308c\u307e\u3059\u3002\u5909\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u8abf\u6574\u6e08\u307f R \u4e8c\u4e57\u306f\u3001<\/strong>\u56de\u5e30\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5b50\u306e\u6570\u3092\u8abf\u6574\u3059\u308b R \u4e8c\u4e57\u306e\u4fee\u6b63\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3059\u3002\u6b21\u306e\u3088\u3046\u306b\u8a08\u7b97\u3055\u308c\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u8abf\u6574\u6e08\u307f R <sup>2<\/sup> = 1 \u2013 [(1-R <sup>2<\/sup> )*(n-1)\/(nk-1)]<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong><sup>R2<\/sup><\/strong> : \u30e2\u30c7\u30eb\u306e<sup>R2<\/sup><\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>n<\/strong> : \u89b3\u6e2c\u5024\u306e\u6570<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>k<\/strong> : \u4e88\u6e2c\u5b50\u5909\u6570\u306e\u6570<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><sup>R2 \u306f<\/sup>\u30e2\u30c7\u30eb\u306b\u4e88\u6e2c\u5b50\u3092\u8ffd\u52a0\u3059\u308b\u3068\u5e38\u306b\u5897\u52a0\u3059\u308b\u305f\u3081\u3001<em>\u30e2\u30c7\u30eb\u5185\u306e\u4e88\u6e2c\u5b50\u306e\u6570\u306b\u57fa\u3065\u3044\u3066\u8abf\u6574\u3055\u308c\u305f\u8abf\u6574<\/em>\u3055\u308c\u305f<sup>R2 \u306f<\/sup>\u3001\u30e2\u30c7\u30eb\u304c\u3069\u306e\u7a0b\u5ea6\u6709\u7528\u3067\u3042\u308b\u304b\u3092\u793a\u3059\u6307\u6a19\u3068\u3057\u3066\u6a5f\u80fd\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\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u8abf\u6574\u3055\u308c\u305f<sup>R2<\/sup>\u3092\u8a08\u7b97\u3059\u308b 2 \u3064\u306e\u4f8b\u3092\u793a\u3057\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u95a2\u9023:<\/strong><\/span> <a href=\"https:\/\/statorials.org\/ja\/\u826f\u597d\u306ar\u4e8c\u4e57\u5024\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u9069\u5207\u306a R \u4e8c\u4e57\u5024\u3068\u306f\u4f55\u3067\u3059\u304b?<\/a><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 1: sklearn \u3092\u4f7f\u7528\u3057\u3066\u8abf\u6574\u6e08\u307f R \u4e8c\u4e57\u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u3001sklearn \u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u8fd1\u4f3c R \u4e8c\u4e57\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/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> 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;\">#fit regression model\n<\/span>model = <span style=\"color: #3366ff;\">LinearRegression<\/span> ()\nx, y = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]], data.hp\nmodel. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n\n<span style=\"color: #008080;\">#display adjusted R-squared\n<\/span>1 - (1-model. <span style=\"color: #3366ff;\">score<\/span> (X, y))*( <span style=\"color: #3366ff;\">len<\/span> (y)-1)\/( <span style=\"color: #3366ff;\">len<\/span> (y)-X. <span style=\"color: #3366ff;\">shape<\/span> [1]-1)\n\n0.7787005290062521<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u8abf\u6574\u3055\u308c\u305f R \u4e8c\u4e57\u306f<strong>0.7787<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4f8b 2: \u7d71\u8a08\u30e2\u30c7\u30eb\u3092\u4f7f\u7528\u3057\u3066\u8abf\u6574\u6e08\u307f R \u4e8c\u4e57\u3092\u8a08\u7b97\u3059\u308b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u306f\u3001\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u8fd1\u4f3c\u3057\u3001statsmodels \u3092\u4f7f\u7528\u3057\u3066\u30e2\u30c7\u30eb\u306e\u8fd1\u4f3c R \u4e8c\u4e57\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;\"> <strong><span style=\"color: #008000;\"><span style=\"color: #000000;\"><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm<\/span>\nimport<\/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;\">#fit regression model\n<\/span>x, y = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]], data.hp\nX = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (X)\nmodel = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#display adjusted R-squared\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">model.rsquared_adj<\/span> )\n\n0.7787005290062521<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u30e2\u30c7\u30eb\u306e\u8abf\u6574\u3055\u308c\u305f R \u4e8c\u4e57\u306f<strong>0.7787<\/strong>\u3067\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u3001\u3053\u308c\u306f\u524d\u306e\u4f8b\u306e\u7d50\u679c\u3068\u4e00\u81f4\u3057\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\/python\u3066\u3099\u306e\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener noreferrer\">Python \u3067\u5358\u7d14\u306a\u7dda\u5f62\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/\u7dda\u5f62\u56de\u5e30python\/\" target=\"_blank\" rel=\"noopener noreferrer\">Python \u3067\u91cd\u56de\u5e30\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5<\/a><br \/><a href=\"https:\/\/statorials.org\/ja\/python\u306eaic\/\" target=\"_blank\" rel=\"noopener\">Python \u3067\u56de\u5e30\u30e2\u30c7\u30eb\u306e AIC \u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>R2 \u306f\u3001\u591a\u304f\u306e\u5834\u5408R2\u3068\u66f8\u304b\u308c\u3001\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u306b\u3088\u3063\u3066\u8aac\u660e\u3067\u304d\u308b\u5fdc\u7b54\u5909\u6570\u306e\u5206\u6563\u306e\u5272\u5408\u3067\u3059\u3002 R \u4e8c\u4e57\u306e\u5024\u306e\u7bc4\u56f2\u306f 0 \u304b\u3089 1 \u3067\u3059\u3002\u5024 0 \u306f\u3001\u5fdc\u7b54\u5909\u6570\u304c\u4e88\u6e2c\u5909\u6570\u306b\u3088\u3063\u3066\u307e\u3063\u305f\u304f\u8aac\u660e\u3067\u304d\u306a\u3044\u3053\u3068\u3092\u793a\u3057\u3001\u5024 [&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-1201","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\u8abf\u6574\u6e08\u307f R \u4e8c\u4e57\u3092\u8a08\u7b97\u3059\u308b\u65b9\u6cd5 - Statology<\/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\u8abf\u6574\u6e08\u307f R 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