{"id":3817,"date":"2023-07-15T09:21:43","date_gmt":"2023-07-15T09:21:43","guid":{"rendered":"https:\/\/statorials.org\/cn\/sklearn%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0\/"},"modified":"2023-07-15T09:21:43","modified_gmt":"2023-07-15T09:21:43","slug":"sklearn%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/sklearn%e5%9b%9e%e5%bd%92%e7%b3%bb%e6%95%b0\/","title":{"rendered":"\u5982\u4f55\u4ece scikit-learn \u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u4ece Python \u4e2d\u4f7f\u7528 scikit-learn \u6784\u5efa\u7684\u56de\u5f52\u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570\uff1a<\/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;\">\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728\u5b9e\u8df5\u4e2d\u4f7f\u7528\u6b64\u8bed\u6cd5\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u793a\u4f8b\uff1a\u4ece Scikit-Learn \u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u5047\u8bbe\u6211\u4eec\u6709\u4ee5\u4e0b pandas DataFrame\uff0c\u5176\u4e2d\u5305\u542b\u6709\u5173\u73ed\u7ea7 11 \u540d\u5b66\u751f\u7684\u5b66\u4e60\u65f6\u95f4\u3001\u51c6\u5907\u8003\u8bd5\u6b21\u6570\u4ee5\u53ca\u671f\u672b\u8003\u8bd5\u6210\u7ee9\u7684\u4fe1\u606f\uff1a<\/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;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u6765\u62df\u5408<a href=\"https:\/\/statorials.org\/cn\/\u591a\u5143\u7ebf\u6027\u56de\u5f52-1\/\" target=\"_blank\" rel=\"noopener\">\u591a\u5143\u7ebf\u6027\u56de\u5f52\u6a21\u578b\uff0c<\/a>\u4f7f\u7528<strong>\u5c0f\u65f6\u6570<\/strong>\u548c<strong>\u8003\u8bd5<\/strong>\u4f5c\u4e3a\u9884\u6d4b\u53d8\u91cf\uff0c<strong>\u5206\u6570<\/strong>\u4f5c\u4e3a\u54cd\u5e94\u53d8\u91cf\uff1a<\/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;\">\u7136\u540e\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u6765\u63d0\u53d6<strong>\u5b66\u65f6<\/strong>\u548c<strong>\u8003\u8bd5<\/strong>\u7684\u56de\u5f52\u7cfb\u6570\uff1a<\/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;\">\u4ece\u7ed3\u679c\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230\u6a21\u578b\u4e2d\u4e24\u4e2a\u9884\u6d4b\u53d8\u91cf\u7684\u56de\u5f52\u7cfb\u6570\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u5c0f\u65f6<\/strong>\u7cfb\u6570\uff1a5.794521<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u8003\u8bd5<\/strong>\u7cfb\u6570\uff1a-1.157647<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5982\u679c\u9700\u8981\uff0c\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\u4ece\u56de\u5f52\u6a21\u578b\u4e2d\u63d0\u53d6\u539f\u59cb\u503c\uff1a<\/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;\">\u4f7f\u7528\u6bcf\u4e2a\u503c\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u62df\u5408\u56de\u5f52\u6a21\u578b\u7684\u65b9\u7a0b\uff1a<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5206\u6570 = 70.483 + 5.795\uff08\u5c0f\u65f6\uff09\u2013 1.158\uff08\u8003\u8bd5\uff09<\/span><\/p>\n<p><span style=\"color: #000000;\">\u7136\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u8fd9\u4e2a\u65b9\u7a0b\u6839\u636e\u5b66\u4e60\u65f6\u95f4\u548c\u7ec3\u4e60\u8003\u8bd5\u6b21\u6570\u6765\u9884\u6d4b\u5b66\u751f\u7684\u671f\u672b\u8003\u8bd5\u6210\u7ee9\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u5b66\u4e60\u4e86 3 \u4e2a\u5c0f\u65f6\u5e76\u53c2\u52a0\u4e86 2 \u6b21\u9884\u5907\u8003\u8bd5\u7684\u5b66\u751f\u6700\u7ec8\u6210\u7ee9\u5e94\u4e3a<strong>85.55<\/strong> \uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5206\u6570 = 70.483 + 5.795\uff08\u5c0f\u65f6\uff09\u2013 1.158\uff08\u8003\u8bd5\uff09<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5206\u6570 = 70.483 + 5.795(3) \u2013 1.158(2)<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5206\u6570 = 85.55<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\"><strong>\u76f8\u5173\uff1a<\/strong><a href=\"https:\/\/statorials.org\/cn\/\u5982\u4f55\u89e3\u91ca\u56de\u5f52\u7cfb\u6570\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u91ca\u56de\u5f52\u7cfb\u6570<\/a><\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u64cd\u4f5c\uff1a<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/cn\/python-\u4e2d\u7684\u7b80\u5355\u7ebf\u6027\u56de\u5f52\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u7b80\u5355\u7ebf\u6027\u56de\u5f52<\/a><br \/> <a href=\"https:\/\/statorials.org\/cn\/\u7ebf\u6027\u56de\u5f52-python\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u591a\u5143\u7ebf\u6027\u56de\u5f52<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u87d2\u86c7\u4e2d\u7684aic\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u7528Python\u8ba1\u7b97\u56de\u5f52\u6a21\u578b\u7684AIC<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u60a8\u53ef\u4ee5\u4f7f\u7528\u4ee5\u4e0b\u57fa\u672c\u8bed\u6cd5\u4ece Python \u4e2d\u4f7f\u7528 scikit-learn \u6784\u5efa\u7684\u56de\u5f52\u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570\uff1a p. [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-3817","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u5982\u4f55\u4ece Scikit-Learn \u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570 - Statorials<\/title>\n<meta name=\"description\" content=\"\u672c\u6559\u7a0b\u901a\u8fc7\u4e00\u4e2a\u793a\u4f8b\u89e3\u91ca\u4e86\u5982\u4f55\u4ece\u4f7f\u7528 scikit-learn \u6784\u5efa\u7684\u56de\u5f52\u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570\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\/cn\/sklearn\u56de\u5f52\u7cfb\u6570\/\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u5982\u4f55\u4ece Scikit-Learn \u6a21\u578b\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570 - 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