{"id":3008,"date":"2023-07-19T15:56:18","date_gmt":"2023-07-19T15:56:18","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d1%81%d0%b2%d0%be%d0%b4%d0%ba%d0%b0-%d0%bf%d0%be-%d0%bb%d0%b8%d0%bd%d0%b5%d0%b8%d0%bd%d0%be%d0%b8-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%81%d0%b8%d0%b8-sklearn\/"},"modified":"2023-07-19T15:56:18","modified_gmt":"2023-07-19T15:56:18","slug":"%d1%81%d0%b2%d0%be%d0%b4%d0%ba%d0%b0-%d0%bf%d0%be-%d0%bb%d0%b8%d0%bd%d0%b5%d0%b8%d0%bd%d0%be%d0%b8-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%81%d0%b8%d0%b8-sklearn","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d1%81%d0%b2%d0%be%d0%b4%d0%ba%d0%b0-%d0%bf%d0%be-%d0%bb%d0%b8%d0%bd%d0%b5%d0%b8%d0%bd%d0%be%d0%b8-%d1%80%d0%b5%d0%b3%d1%80%d0%b5%d1%81%d1%81%d0%b8%d0%b8-sklearn\/","title":{"rendered":"\u041a\u0430\u043a \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0438\u0437 scikit-learn"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435\u0442 \u043f\u043e\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438, \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u043e\u0439 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e <a href=\"https:\/\/scikit-learn.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">scikit-learn<\/a> \u0432 Python.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041a \u0441\u043e\u0436\u0430\u043b\u0435\u043d\u0438\u044e, scikit-learn \u043d\u0435 \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u0435\u0442 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u043e \u0432\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u044b\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0441\u0432\u043e\u0434\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438 \u043e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u043e\u0431\u044b\u0447\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u0432 <a href=\"https:\/\/statorials.org\/ru\/\u0432\u044b\u0432\u043e\u0434-\u043f\u0440\u043e\u0442\u0438\u0432-\u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u043d\u0438\u044f\/\" target=\"_blank\" rel=\"noopener\">\u0446\u0435\u043b\u044f\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0418\u0442\u0430\u043a, \u0435\u0441\u043b\u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u043d\u0430 Python, \u0443 \u0432\u0430\u0441 \u0435\u0441\u0442\u044c \u0434\u0432\u0430 \u0432\u0430\u0440\u0438\u0430\u043d\u0442\u0430:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1.<\/strong> \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u043d\u044b\u0435 \u0444\u0443\u043d\u043a\u0446\u0438\u0438 scikit-learn.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2.<\/strong> \u0412\u043c\u0435\u0441\u0442\u043e \u044d\u0442\u043e\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 <a href=\"https:\/\/www.statsmodels.org\/stable\/index.html\" target=\"_blank\" rel=\"noopener\">\u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u043f\u0440\u0438\u043c\u0435\u0440\u0430\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u043d\u043e, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043a\u0430\u0436\u0434\u044b\u0439 \u043c\u0435\u0442\u043e\u0434 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 \u0441\u043e \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u043c DataFrame pandas:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#createDataFrame\n<span style=\"color: #000000;\">df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">x1<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4],\n                   ' <span style=\"color: #ff0000;\">x2<\/span> ': [1, 3, 3, 5, 2, 2, 1, 1, 0, 3, 4],\n                   ' <span style=\"color: #ff0000;\">y<\/span> ': [76, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90]})\n\n<span style=\"color: #008080;\">#view first five rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n       x1 x2 y\n0 1 1 76\n1 2 3 78\n2 2 3 85\n3 4 5 88\n4 2 2 72\n<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u041c\u0435\u0442\u043e\u0434 1: \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0438\u0437 Scikit-Learn<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434, \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0434\u043e\u0433\u043d\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u044c <a href=\"https:\/\/statorials.org\/ru\/\u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u0430\u044f-\u043b\u0438\u043d\u0435\u0438\u043d\u0430\u044f-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044f\/\" target=\"_blank\" rel=\"noopener\">\u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438<\/a> \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e scikit-learn:<\/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;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ']], df. <span style=\"color: #3366ff;\">y<\/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;\"><span style=\"color: #000000;\">\u0417\u0430\u0442\u0435\u043c \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u0434\u043b\u044f \u0438\u0437\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u044f \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u043e\u0432 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0438\u0437 \u043c\u043e\u0434\u0435\u043b\u0438, \u0430 \u0442\u0430\u043a\u0436\u0435 <a href=\"https:\/\/statorials.org\/ru\/\u0445\u043e\u0440\u043e\u0448\u0435\u0435-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435-r-\u0432-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0435\/\" target=\"_blank\" rel=\"noopener\">\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0430<\/a> \u043c\u043e\u0434\u0435\u043b\u0438:<\/span><\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\"><span style=\"color: #000000;\"><span style=\"color: #008080;\">#display regression coefficients and R-squared value of model<\/span>\n<span style=\"color: #008000;\">print<\/span> (model. <span style=\"color: #3366ff;\">intercept_<\/span> , model. <span style=\"color: #3366ff;\">coef_<\/span> , model. <span style=\"color: #3366ff;\">score<\/span> (X, y))\n\n70.4828205704 [5.7945 -1.1576] 0.766742556527\n<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u044d\u0442\u043e\u0442 \u0432\u044b\u0432\u043e\u0434, \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0443\u0440\u0430\u0432\u043d\u0435\u043d\u0438\u0435 \u0434\u043b\u044f \u043f\u043e\u0434\u043e\u0431\u0440\u0430\u043d\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438:<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0443 = 70,48 + 5,79\u0445 <sub>1<\/sub> \u2013 1,16\u0445 <sub>2<\/sub><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0422\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u043d\u043e \u0432\u0438\u0434\u0435\u0442\u044c, \u0447\u0442\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 R <sup>2<\/sup> \u043c\u043e\u0434\u0435\u043b\u0438 \u0441\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 76,67.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u042d\u0442\u043e \u043e\u0437\u043d\u0430\u0447\u0430\u0435\u0442, \u0447\u0442\u043e <strong>76,67%<\/strong> \u0432\u0430\u0440\u0438\u0430\u0446\u0438\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043e\u0442\u043a\u043b\u0438\u043a\u0430 \u043c\u043e\u0436\u043d\u043e \u043e\u0431\u044a\u044f\u0441\u043d\u0438\u0442\u044c \u0434\u0432\u0443\u043c\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u043c\u0438-\u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430\u043c\u0438 \u0432 \u043c\u043e\u0434\u0435\u043b\u0438.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0425\u043e\u0442\u044f \u044d\u0442\u043e\u0442 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442 \u043f\u043e\u043b\u0435\u0437\u0435\u043d, \u043c\u044b \u0432\u0441\u0435 \u0435\u0449\u0435 \u043d\u0435 \u0437\u043d\u0430\u0435\u043c <a href=\"https:\/\/statorials.org\/ru\/\u043f\u0440\u043e\u0441\u0442\u043e\u0435-\u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u043e-\u043f\u043e-\u043f\u043e\u043d\u0438\u043c\u0430\u043d\u0438\u044e-f-\u0442\u0435\u0441\u0442\u0430-\u043d\u0430-\u043e\u0431\u0449\u0443\u044e-\u0437\u043d\u0430\u0447\u0438\u043c\u043e\u0441\u0442\u044c-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438\/\" target=\"_blank\" rel=\"noopener\">\u043e\u0431\u0449\u0443\u044e F-\u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0443<\/a> \u043c\u043e\u0434\u0435\u043b\u0438, p-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u044b\u0445 <a href=\"https:\/\/statorials.org\/ru\/\u043a\u0430\u043a-\u0438\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0438\u0440\u043e\u0432\u0430\u0442\u044c-\u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u044b-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438\/\" target=\"_blank\" rel=\"noopener\">\u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u043e\u0432 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438<\/a> \u0438 \u0434\u0440\u0443\u0433\u0438\u0435 \u043f\u043e\u043b\u0435\u0437\u043d\u044b\u0435 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043c\u043e\u0433\u0443\u0442 \u043f\u043e\u043c\u043e\u0447\u044c \u043d\u0430\u043c \u043f\u043e\u043d\u044f\u0442\u044c, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0445\u043e\u0440\u043e\u0448\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u043c\u043e\u0434\u0435\u043b\u0438. \u043d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445.\u043d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0421\u043f\u043e\u0441\u043e\u0431 2. \u041f\u043e\u043b\u0443\u0447\u0438\u0442\u0435 \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0438\u0437 Statsmodels.<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0415\u0441\u043b\u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0432 Python, \u043b\u0443\u0447\u0448\u0435 \u0432\u0441\u0435\u0433\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043f\u0430\u043a\u0435\u0442 <strong>statsmodels<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u044d\u0442\u043e\u0442 \u043f\u0430\u043a\u0435\u0442 \u0434\u043b\u044f \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u044f \u0442\u043e\u0439 \u0436\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0439 \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0439 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438, \u0447\u0442\u043e \u0438 \u0432 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435, \u0438 \u0438\u0437\u0432\u043b\u0435\u0447\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n\n<span style=\"color: #008080;\">#define response variable\n<\/span>y = df[' <span style=\"color: #ff0000;\">y<\/span> ']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = df[[' <span style=\"color: #ff0000;\">x1<\/span> ', ' <span style=\"color: #ff0000;\">x2<\/span> ']]\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 linear 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 model summary\n<\/span><span style=\"color: #008000;\">print<\/span> ( <span style=\"color: #3366ff;\">model.summary<\/span> ())\n\n                            OLS Regression Results                            \n==================================================== ============================\nDept. Variable: y R-squared: 0.767\nModel: OLS Adj. R-squared: 0.708\nMethod: Least Squares F-statistic: 13.15\nDate: Fri, 01 Apr 2022 Prob (F-statistic): 0.00296\nTime: 11:10:16 Log-Likelihood: -31.191\nNo. Comments: 11 AIC: 68.38\nDf Residuals: 8 BIC: 69.57\nDf Model: 2                                         \nCovariance Type: non-robust                                         \n==================================================== ============================\n                 coef std err t P&gt;|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 70.4828 3.749 18.803 0.000 61.839 79.127\nx1 5.7945 1.132 5.120 0.001 3.185 8.404\nx2 -1.1576 1.065 -1.087 0.309 -3.613 1.298\n==================================================== ============================\nOmnibus: 0.198 Durbin-Watson: 1.240\nProb(Omnibus): 0.906 Jarque-Bera (JB): 0.296\nSkew: -0.242 Prob(JB): 0.862\nKurtosis: 2.359 Cond. No. 10.7\n==================================================== ============================\n<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">\u041e\u0431\u0440\u0430\u0442\u0438\u0442\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435, \u0447\u0442\u043e \u043a\u043e\u044d\u0444\u0444\u0438\u0446\u0438\u0435\u043d\u0442\u044b \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0430 \u0441\u043e\u0432\u043f\u0430\u0434\u0430\u044e\u0442 \u0441 \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u043d\u044b\u043c\u0438 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e scikit-learn, \u043d\u043e \u0443 \u043d\u0430\u0441 \u0442\u0430\u043a\u0436\u0435 \u0435\u0441\u0442\u044c \u043c\u0430\u0441\u0441\u0430 \u0434\u0440\u0443\u0433\u0438\u0445 \u043f\u043e\u043b\u0435\u0437\u043d\u044b\u0445 \u043f\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u0435\u0439 \u0434\u043b\u044f \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0443\u0432\u0438\u0434\u0435\u0442\u044c p-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0439 \u043e\u0442\u0434\u0435\u043b\u044c\u043d\u043e\u0439 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u043d\u043e\u0439 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">p-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0434\u043b\u044f x <sub>1<\/sub> = 0,001<\/span><\/li>\n<li> <span style=\"color: #000000;\">p-\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 \u0434\u043b\u044f x <sub>2<\/sub> = 0,309<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u043c \u0443\u0432\u0438\u0434\u0435\u0442\u044c \u043e\u0431\u0449\u0443\u044e F-\u0441\u0442\u0430\u0442\u0438\u0441\u0442\u0438\u043a\u0443 \u043c\u043e\u0434\u0435\u043b\u0438, <a href=\"https:\/\/statorials.org\/ru\/\u0441\u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u0430\u044f-\u0438\u043d\u0442\u0435\u0440\u043f\u0440\u0435\u0442\u0430\u0446\u0438\u044f-r-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0430\/\" target=\"_blank\" rel=\"noopener\">\u0441\u043a\u043e\u0440\u0440\u0435\u043a\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0435 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 R-\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0430<\/a> , <a href=\"https:\/\/statorials.org\/ru\" target=\"_blank\" rel=\"noopener\">\u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0435 AIC<\/a> \u043c\u043e\u0434\u0435\u043b\u0438 \u0438 \u043c\u043d\u043e\u0433\u043e\u0435 \u0434\u0440\u0443\u0433\u043e\u0435.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0414\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0412 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0445 \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0430\u0445 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0442\u044c \u0434\u0440\u0443\u0433\u0438\u0435 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u043d\u044b\u0435 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438 \u0432 Python:<\/span><\/p>\n<p> <a href=\"https:\/\/statorials.org\/ru\/\u043f\u0440\u043e\u0441\u0442\u0430\u044f-\u043b\u0438\u043d\u0435\u0438\u043d\u0430\u044f-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044f-\u0432-python\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u0441\u0442\u0443\u044e \u043b\u0438\u043d\u0435\u0439\u043d\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/ru\/\u043b\u0438\u043d\u0435\u0438\u043d\u0430\u044f-\u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044f-python\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u041a\u0430\u043a \u0432\u044b\u043f\u043e\u043b\u043d\u0438\u0442\u044c \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u0443\u044e \u043b\u0438\u043d\u0435\u0439\u043d\u0443\u044e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u044e \u0432 Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/ru\/\u0430\u0438\u043a-\u0432-\u043f\u0438\u0442\u043e\u043d\u0435\/\" target=\"_blank\" rel=\"noopener\">\u041a\u0430\u043a \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u0442\u044c AIC \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u044b\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0432 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0427\u0430\u0441\u0442\u043e \u0432\u0430\u043c \u043c\u043e\u0436\u0435\u0442 \u043f\u043e\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u0442\u044c\u0441\u044f \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438, \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u043e\u0439 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e scikit-learn \u0432 Python. \u041a \u0441\u043e\u0436\u0430\u043b\u0435\u043d\u0438\u044e, scikit-learn \u043d\u0435 \u043f\u0440\u0435\u0434\u043b\u0430\u0433\u0430\u0435\u0442 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u043e \u0432\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u044b\u0445 \u0444\u0443\u043d\u043a\u0446\u0438\u0439 \u0434\u043b\u044f \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0441\u0432\u043e\u0434\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438 \u043e \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u043e\u0431\u044b\u0447\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0442\u043e\u043b\u044c\u043a\u043e \u0432 \u0446\u0435\u043b\u044f\u0445 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f . \u0418\u0442\u0430\u043a, \u0435\u0441\u043b\u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u043d\u0430 Python, \u0443 \u0432\u0430\u0441 \u0435\u0441\u0442\u044c \u0434\u0432\u0430 [&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-3008","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>\u041a\u0430\u043a \u043f\u043e\u043b\u0443\u0447\u0438\u0442\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u043c\u043e\u0434\u0435\u043b\u0438 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u0438 \u0438\u0437 Scikit-Learn \u2014 \u0421\u0442\u0430\u0442\u043e\u0440\u0438\u0430\u043b\u044b<\/title>\n<meta name=\"description\" content=\"\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043e\u0431\u044a\u044f\u0441\u043d\u044f\u0435\u0442\u0441\u044f, \u043a\u0430\u043a \u0438\u0437\u0432\u043b\u0435\u0447\u044c \u0441\u0432\u043e\u0434\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u0438\u0437 \u0440\u0435\u0433\u0440\u0435\u0441\u0441\u0438\u043e\u043d\u043d\u043e\u0439 \u043c\u043e\u0434\u0435\u043b\u0438, \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u043e\u0439 \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e scikit-learn, \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043f\u0440\u0438\u043c\u0435\u0440\u0430.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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