{"id":4061,"date":"2023-07-13T21:08:59","date_gmt":"2023-07-13T21:08:59","guid":{"rendered":"https:\/\/statorials.org\/cn\/%e5%a4%9a%e9%a1%b9%e5%bc%8f%e5%9b%9e%e5%bd%92-sklearn\/"},"modified":"2023-07-13T21:08:59","modified_gmt":"2023-07-13T21:08:59","slug":"%e5%a4%9a%e9%a1%b9%e5%bc%8f%e5%9b%9e%e5%bd%92-sklearn","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/%e5%a4%9a%e9%a1%b9%e5%bc%8f%e5%9b%9e%e5%bd%92-sklearn\/","title":{"rendered":"\u5982\u4f55\u4f7f\u7528 scikit-learn \u6267\u884c\u591a\u9879\u5f0f\u56de\u5f52"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u5f53\u9884\u6d4b\u53d8\u91cf\u548c<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u54cd\u5e94\u53d8\u91cf<\/a>\u4e4b\u95f4\u7684\u5173\u7cfb\u662f\u975e\u7ebf\u6027\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528<a href=\"https:\/\/statorials.org\/cn\/\u591a\u9879\u5f0f\u56de\u5f521\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u591a\u9879\u5f0f\u56de\u5f52<\/a>\u6280\u672f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u8fd9\u79cd\u7c7b\u578b\u7684\u56de\u5f52\u91c7\u7528\u4ee5\u4e0b\u5f62\u5f0f\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">Y = \u03b2 <sub>0<\/sub> <sup>+<\/sup> \u03b2 <sub>1<\/sub> X + \u03b2 <sub>2<\/sub> X <sup>2<\/sup> + \u2026 + \u03b2 <sub>h<\/sub><\/span><\/p>\n<p><span style=\"color: #000000;\">\u5176\u4e2d<em>h<\/em>\u662f\u591a\u9879\u5f0f\u7684\u201c\u6b21\u6570\u201d\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u5206\u6b65\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528 sklearn \u5728 Python \u4e2d\u6267\u884c\u591a\u9879\u5f0f\u56de\u5f52\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u7b2c 1 \u6b65\uff1a\u521b\u5efa\u6570\u636e<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u521b\u5efa\u4e24\u4e2a NumPy \u6570\u7ec4\u6765\u4fdd\u5b58\u9884\u6d4b\u53d8\u91cf\u548c\u54cd\u5e94\u53d8\u91cf\u7684\u503c\uff1a<\/span> <\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n\n<span style=\"color: #008080;\">#define predictor and response variables\n<\/span>x = np. <span style=\"color: #3366ff;\">array<\/span> ([2, 3, 4, 5, 6, 7, 7, 8, 9, 11, 12])\ny = np. <span style=\"color: #3366ff;\">array<\/span> ([18, 16, 15, 17, 20, 23, 25, 28, 31, 30, 29])\n\n<span style=\"color: #008080;\">#create scatterplot to visualize relationship between x and y\n<\/span>plt. <span style=\"color: #3366ff;\">scatter<\/span> (x,y)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-32377 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polysk1.png\" alt=\"\" width=\"502\" height=\"381\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u4ece\u6563\u70b9\u56fe\u4e2d\uff0c\u6211\u4eec\u53ef\u4ee5\u770b\u5230x\u548cy\u4e4b\u95f4\u7684\u5173\u7cfb\u4e0d\u662f\u7ebf\u6027\u7684\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u56e0\u6b64\uff0c\u6700\u597d\u5c06\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b\u62df\u5408\u5230\u6570\u636e\u4ee5\u6355\u83b7\u4e24\u4e2a\u53d8\u91cf\u4e4b\u95f4\u7684\u975e\u7ebf\u6027\u5173\u7cfb\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 2\uff1a\u62df\u5408\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b<\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528 sklearn \u51fd\u6570\u6765\u62df\u5408\u6b64\u6570\u636e\u96c6\u7684 3 \u6b21\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #008000;\"><span style=\"color: #3366ff;\">preprocessing<\/span> import<\/span> PolynomialFeatures\n<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;\">#specify degree of 3 for polynomial regression model\n#include bias=False means don't force y-intercept to equal zero<\/span>\npoly = PolynomialFeatures(degree= <span style=\"color: #008000;\">3<\/span> , include_bias= <span style=\"color: #008000;\">False<\/span> )\n\n<span style=\"color: #008080;\">#reshape data to work properly with sklearn\n<\/span>poly_features = poly. <span style=\"color: #3366ff;\">fit_transform<\/span> ( <span style=\"color: #3366ff;\">x.reshape<\/span> (-1, 1))\n\n<span style=\"color: #008080;\">#fit polynomial regression model\n<\/span>poly_reg_model = LinearRegression()\npoly_reg_model. <span style=\"color: #3366ff;\">fit<\/span> (poly_features,y)\n\n<span style=\"color: #008080;\">#display model coefficients\n<\/span><span style=\"color: #008000;\">print<\/span> (poly_reg_model. <span style=\"color: #3366ff;\">intercept_<\/span> , poly_reg_model. <span style=\"color: #3366ff;\">coef_<\/span> )\n\n33.62640037532282 [-11.83877127 2.25592957 -0.10889554]\n<\/strong><\/span><\/pre>\n<p><span style=\"color: #000000;\">\u4f7f\u7528\u6700\u540e\u4e00\u884c\u663e\u793a\u7684\u6a21\u578b\u7cfb\u6570\uff0c\u6211\u4eec\u53ef\u4ee5\u7f16\u5199\u62df\u5408\u7684\u591a\u9879\u5f0f\u56de\u5f52\u65b9\u7a0b\u5982\u4e0b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0.109x <sup>3<\/sup> + 2.256x <sup>2<\/sup> \u2013 11.839x + 33.626<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u7ed9\u5b9a\u9884\u6d4b\u53d8\u91cf\u7684\u7ed9\u5b9a\u503c\u7684\u60c5\u51b5\u4e0b\uff0c\u8be5\u65b9\u7a0b\u53ef\u7528\u4e8e\u67e5\u627e\u54cd\u5e94\u53d8\u91cf\u7684\u671f\u671b\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4f8b\u5982\uff0c\u5982\u679c x \u4e3a 4\uff0c\u5219\u54cd\u5e94\u53d8\u91cf y \u7684\u9884\u671f\u503c\u4e3a 15.39\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\">y = -0.109(4) <sup>3<\/sup> + 2.256(4) <sup>2<\/sup> \u2013 11.839(4) + 33.626= 15.39<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u8981\u62df\u5408\u5177\u6709\u4e0d\u540c\u9636\u6570\u7684\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b\uff0c\u53ea\u9700\u66f4\u6539<strong>PolynomialFeatures()<\/strong>\u51fd\u6570\u4e2d<strong>\u9636\u6570<\/strong>\u53c2\u6570\u7684\u503c\u5373\u53ef\u3002<\/span><\/p>\n<h2><strong><span style=\"color: #000000;\">\u6b65\u9aa4 3\uff1a\u53ef\u89c6\u5316\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b<\/span><\/strong><\/h2>\n<p><span style=\"color: #000000;\"><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u56fe\u6765\u53ef\u89c6\u5316\u62df\u5408\u539f\u59cb\u6570\u636e\u70b9\u7684\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b\uff1a<\/span><\/span> <\/p>\n<pre style=\"background-color: #e5e5e5; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#use model to make predictions on response variable\n<\/span>y_predicted = poly_reg_model. <span style=\"color: #3366ff;\">predict<\/span> (poly_features)\n\n<span style=\"color: #008080;\">#create scatterplot of x vs. y\n<\/span>plt. <span style=\"color: #3366ff;\">scatter<\/span> (x,y)\n\n<span style=\"color: #008080;\">#add line to show fitted polynomial regression model\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (x,y_predicted,color=' <span style=\"color: #ff0000;\">purple<\/span> ')\n<\/strong><\/span><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-32378 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polysk2.png\" alt=\"\" width=\"523\" height=\"392\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u4ece\u56fe\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u591a\u9879\u5f0f\u56de\u5f52\u6a21\u578b\u4f3c\u4e4e\u5f88\u597d\u5730\u62df\u5408\u4e86\u6570\u636e\uff0c\u6ca1\u6709<a href=\"https:\/\/statorials.org\/cn\/\u673a\u5668\u5b66\u4e60\u8fc7\u5ea6\u62df\u5408\/\" target=\"_blank\" rel=\"noopener\">\u51fa\u73b0\u8fc7\u62df\u5408\u7684\u60c5\u51b5<\/a>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\"><strong>\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5<a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.preprocessing.PolynomialFeatures.html\" target=\"_blank\" rel=\"noopener\">\u5728\u6b64\u5904<\/a>\u627e\u5230 sklearn <strong>PolynomialFeatures()<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n<h2><span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">\u5176\u4ed6\u8d44\u6e90<\/span><\/strong><\/span><\/h2>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u4f7f\u7528 sklearn \u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n<p><a href=\"https:\/\/statorials.org\/cn\/sklearn\u56de\u5f52\u7cfb\u6570\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u4ecesklearn\u4e2d\u63d0\u53d6\u56de\u5f52\u7cfb\u6570<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/\u5e73\u8861\u7cbe\u5ea6-python-sklearn\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u4f7f\u7528sklearn\u8ba1\u7b97\u5e73\u8861\u7cbe\u5ea6<\/a><br \/><a href=\"https:\/\/statorials.org\/cn\/sklearn\u5206\u7c7b\u62a5\u544a\/\" target=\"_blank\" rel=\"noopener\">\u5982\u4f55\u89e3\u8bfbSklearn\u4e2d\u7684\u5206\u7c7b\u62a5\u544a<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5f53\u9884\u6d4b\u53d8\u91cf\u548c\u54cd\u5e94\u53d8\u91cf\u4e4b\u95f4\u7684\u5173\u7cfb\u662f\u975e\u7ebf\u6027\u65f6\uff0c\u53ef\u4ee5\u4f7f\u7528\u591a\u9879\u5f0f\u56de\u5f52\u6280\u672f\u3002 \u8fd9\u79cd\u7c7b\u578b\u7684\u56de\u5f52\u91c7\u7528\u4ee5\u4e0b\u5f62\u5f0f\uff1a Y = \u03b2  [&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-4061","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\u4f7f\u7528 Scikit-Learn \u6267\u884c\u591a\u9879\u5f0f\u56de\u5f52 - 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