{"id":3812,"date":"2023-07-15T09:21:43","date_gmt":"2023-07-15T09:21:43","guid":{"rendered":"https:\/\/statorials.org\/ja\/sklearn%e5%9b%9e%e5%b8%b0%e4%bf%82%e6%95%b0\/"},"modified":"2023-07-15T09:21:43","modified_gmt":"2023-07-15T09:21:43","slug":"sklearn%e5%9b%9e%e5%b8%b0%e4%bf%82%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/ja\/sklearn%e5%9b%9e%e5%b8%b0%e4%bf%82%e6%95%b0\/","title":{"rendered":"Scikit-learn \u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001Python \u306e scikit-learn \u3067\u69cb\u7bc9\u3055\u308c\u305f\u56de\u5e30\u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\"><span style=\"color: #000000;\">p.d. <span style=\"color: #3366ff;\">DataFrame<\/span> ( <span style=\"color: #008000;\">zip<\/span> ( <span style=\"color: #3366ff;\">X.columns<\/span> , <span style=\"color: #3366ff;\">model.coef_<\/span> ))\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u4f8b\u306f\u3001\u3053\u306e\u69cb\u6587\u3092\u5b9f\u969b\u306b\u4f7f\u7528\u3059\u308b\u65b9\u6cd5\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u4f8b: Scikit-Learn \u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3059\u308b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u30af\u30e9\u30b9\u306e 11 \u4eba\u306e\u751f\u5f92\u304c\u5b66\u7fd2\u3057\u305f\u6642\u9593\u3001\u53d7\u3051\u305f\u4e88\u5099\u8a66\u9a13\u306e\u6570\u3001\u304a\u3088\u3073\u6700\u7d42\u8a66\u9a13\u306e\u6210\u7e3e\u306b\u95a2\u3059\u308b\u60c5\u5831\u3092\u542b\u3080\u6b21\u306e\u30d1\u30f3\u30c0 \u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u304c\u3042\u308b\u3068\u3057\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> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">hours<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4],\n                   ' <span style=\"color: #ff0000;\">exams<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4],\n                   ' <span style=\"color: #ff0000;\">score<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90]})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span><span style=\"color: #008000;\">print<\/span> (df)\n\n    hours exam score\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72\n5 1 2 69\n6 5 1 94\n7 4 1 94\n8 2 0 88\n9 4 3 92\n10 4 4 90<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30b3\u30fc\u30c9\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001<strong>\u6642\u9593<\/strong>\u3068<strong>\u8a66\u9a13\u3092<\/strong>\u4e88\u6e2c\u5909\u6570\u3068\u3057\u3066\u3001<strong>\u30b9\u30b3\u30a2\u3092<\/strong>\u5fdc\u7b54\u5909\u6570\u3068\u3057\u3066\u4f7f\u7528\u3057\u3066<a href=\"https:\/\/statorials.org\/ja\/\u91cd\u7dda\u5f62\u56de\u5e30\/\" target=\"_blank\" rel=\"noopener\">\u91cd\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb<\/a>\u3092\u8fd1\u4f3c\u3067\u304d\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;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LinearRegression\n\n<span style=\"color: #008080;\">#initiate linear regression model\n<\/span>model = LinearRegression()\n\n<span style=\"color: #008080;\">#define predictor and response variables\n<\/span>x, y = df[[' <span style=\"color: #ff0000;\">hours<\/span> ', ' <span style=\"color: #ff0000;\">exams<\/span> ']], df. <span style=\"color: #3366ff;\">score<\/span>\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001<strong>\u6642\u9593<\/strong>\u3068<strong>\u8a66\u9a13<\/strong>\u306e\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3067\u304d\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: #008080;\">#print regression coefficients\n<span style=\"color: #000000;\">p.d. <span style=\"color: #3366ff;\">DataFrame<\/span> ( <span style=\"color: #008000;\">zip<\/span> ( <span style=\"color: #3366ff;\">X.columns<\/span> , <span style=\"color: #3366ff;\">model.coef_<\/span> ))\n\n            0 1\n0 hours 5.794521\n1 exams -1.157647\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u7d50\u679c\u304b\u3089\u3001\u30e2\u30c7\u30eb\u5185\u306e 2 \u3064\u306e\u4e88\u6e2c\u5b50\u5909\u6570\u306e\u56de\u5e30\u4fc2\u6570\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u6642\u9593<\/strong>\u306e\u4fc2\u6570: 5.794521<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u8a66\u9a13<\/strong>\u7528\u4fc2\u6570\uff1a-1.157647<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u3001\u6b21\u306e\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u56de\u5e30\u30e2\u30c7\u30eb\u304b\u3089\u5143\u306e\u5024\u3092\u62bd\u51fa\u3059\u308b\u3053\u3068\u3082\u3067\u304d\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: #008080;\">#print intercept value\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">print<\/span> (model. <span style=\"color: #3366ff;\">intercept_<\/span> )\n\n70.48282057040197\n<\/span><\/span><\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u3053\u308c\u3089\u306e\u5404\u5024\u3092\u4f7f\u7528\u3057\u3066\u3001\u8fd1\u4f3c\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u65b9\u7a0b\u5f0f\u3092\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u30b9\u30b3\u30a2 = 70.483 + 5.795 (\u6642\u9593) \u2013 1.158 (\u8a66\u9a13)<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6b21\u306b\u3001\u3053\u306e\u65b9\u7a0b\u5f0f\u3092\u4f7f\u7528\u3057\u3066\u3001\u52c9\u5f37\u306b\u8cbb\u3084\u3057\u305f\u6642\u9593\u6570\u3068\u53d7\u3051\u305f\u6a21\u64ec\u8a66\u9a13\u306e\u6570\u306b\u57fa\u3065\u3044\u3066\u3001\u751f\u5f92\u306e\u6700\u7d42\u8a66\u9a13\u306e\u6210\u7e3e\u3092\u4e88\u6e2c\u3067\u304d\u307e\u3059\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u305f\u3068\u3048\u3070\u30013 \u6642\u9593\u52c9\u5f37\u3057\u3066 2 \u3064\u306e\u4e88\u5099\u8a66\u9a13\u3092\u53d7\u3051\u305f\u5b66\u751f\u306f\u3001\u6700\u7d42\u6210\u7e3e<strong>85.55<\/strong>\u3092\u53d6\u5f97\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u30b9\u30b3\u30a2 = 70.483 + 5.795 (\u6642\u9593) \u2013 1.158 (\u8a66\u9a13)<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30b9\u30b3\u30a2 = 70.483 + 5.795(3) \u2013 1.158(2)<\/span><\/li>\n<li><span style=\"color: #000000;\">\u30b9\u30b3\u30a2 = 85.55<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>\u95a2\u9023:<\/strong> <a href=\"https:\/\/statorials.org\/ja\/\u56de\u5e30\u4fc2\u6570\u3092\u3068\u3099\u3046\u89e3\u91c8\u3059\u308b\u304b\/\" target=\"_blank\" rel=\"noopener\">\u56de\u5e30\u4fc2\u6570\u306e\u89e3\u91c8\u65b9\u6cd5<\/a><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u8ffd\u52a0\u30ea\u30bd\u30fc\u30b9<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u6b21\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001Python \u3067\u4ed6\u306e\u4e00\u822c\u7684\u306a\u64cd\u4f5c\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u8aac\u660e\u3057\u307e\u3059\u3002<\/span><\/p>\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>\u6b21\u306e\u57fa\u672c\u69cb\u6587\u3092\u4f7f\u7528\u3057\u3066\u3001Python \u306e scikit-learn \u3067\u69cb\u7bc9\u3055\u308c\u305f\u56de\u5e30\u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3067\u304d\u307e\u3059\u3002 p.d. DataFrame ( zip ( X.columns , model.coef_ ))  [&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-3812","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>Scikit-Learn \u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5 - Statology<\/title>\n<meta name=\"description\" content=\"\u3053\u306e\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3067\u306f\u3001scikit-learn \u3067\u69cb\u7bc9\u3055\u308c\u305f\u56de\u5e30\u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5\u3092\u4f8b\u3092\u6319\u3052\u3066\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 rel=\"canonical\" href=\"https:\/\/statorials.org\/ja\/sklearn\u56de\u5e30\u4fc2\u6570\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Scikit-Learn \u30e2\u30c7\u30eb\u304b\u3089\u56de\u5e30\u4fc2\u6570\u3092\u62bd\u51fa\u3059\u308b\u65b9\u6cd5 - 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