{"id":1154,"date":"2023-07-27T11:31:28","date_gmt":"2023-07-27T11:31:28","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d9%84%d9%88%d8%ac%d8%b3%d8%aa%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-27T11:31:28","modified_gmt":"2023-07-27T11:31:28","slug":"%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d9%84%d9%88%d8%ac%d8%b3%d8%aa%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d8%a7%d9%84%d9%84%d9%88%d8%ac%d8%b3%d8%aa%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 (\u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p style=\";text-align:right;direction:rtl\"><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a<\/a> \u0647\u0648 \u0637\u0631\u064a\u0642\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627 \u0644\u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0639\u0646\u062f\u0645\u0627 \u064a\u0643\u0648\u0646 <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a-\u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0627\u062a-\u0627\u0644\u062a\u0641\u0633\u064a\u0631\u064a\u0629\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629<\/a> \u062b\u0646\u0627\u0626\u064a\u064b\u0627.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0637\u0631\u064a\u0642\u0629 \u062a\u0639\u0631\u0641 <em>\u0628\u062a\u0642\u062f\u064a\u0631 \u0627\u0644\u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u0627\u0644\u0642\u0635\u0648\u0649<\/em> \u0644\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0645\u0639\u0627\u062f\u0644\u0629 \u0628\u0627\u0644\u0634\u0643\u0644 \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0633\u062c\u0644[p(X) \/ ( <sub>1<\/sub> -p(X))] = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> X <sub>1<\/sub> + \u03b2 <sub>2<\/sub> X <sub>2<\/sub> + \u2026 + \u03b2 <sub>p<\/sub><\/strong><\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0630\u0647\u0628:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>X <sub>j<\/sub><\/strong> : <sup>\u0627\u0644\u0645\u062a\u063a\u064a\u0631<\/sup> \u0627\u0644\u062a\u0646\u0628\u0626\u064a j<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u03b2 <sub>j<\/sub><\/strong> : \u062a\u0642\u062f\u064a\u0631 \u0645\u0639\u0627\u0645\u0644 \u0627\u0644\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u062a\u0646\u0628\u0626\u064a <sup>j<\/sup><\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u062a\u0646\u0628\u0623 \u0627\u0644\u0635\u064a\u063a\u0629 \u0627\u0644\u0645\u0648\u062c\u0648\u062f\u0629 \u0639\u0644\u0649 \u0627\u0644\u062c\u0627\u0646\u0628 \u0627\u0644\u0623\u064a\u0645\u0646 \u0645\u0646 \u0627\u0644\u0645\u0639\u0627\u062f\u0644\u0629 <strong>\u0628\u0627\u062d\u062a\u0645\u0627\u0644\u0627\u062a \u0627\u0644\u0633\u062c\u0644<\/strong> \u0627\u0644\u062a\u064a \u064a\u0623\u062e\u0630 \u0641\u064a\u0647\u0627 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0627\u0644\u0642\u064a\u0645\u0629 1.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0630\u0644\u0643\u060c \u0639\u0646\u062f\u0645\u0627 \u0646\u0644\u0627\u0626\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0645\u0639\u0627\u062f\u0644\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0644\u062d\u0633\u0627\u0628 \u0627\u062d\u062a\u0645\u0627\u0644 \u0623\u0646 \u062a\u0623\u062e\u0630 \u0645\u0644\u0627\u062d\u0638\u0629 \u0645\u0639\u064a\u0646\u0629 \u0627\u0644\u0642\u064a\u0645\u0629 1:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0635(X) = \u0647 <sup>\u03b2 <sub>0<\/sub> + <sub>\u03b2<\/sub> <sub>1<\/sub> <sub>X<\/sub> <sub>1<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> <sup><sub>2<\/sub> <sub>X<\/sub> <sub>2<\/sub> <sub>+<\/sub> <sub>\u2026<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> \u0635<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0646\u0633\u062a\u062e\u062f\u0645 \u0628\u0639\u062f \u0630\u0644\u0643 \u0639\u062a\u0628\u0629 \u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u0645\u0639\u064a\u0646\u0629 \u0644\u062a\u0635\u0646\u064a\u0641 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0629 \u0639\u0644\u0649 \u0623\u0646\u0647\u0627 1 \u0623\u0648 0.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0644\u0642\u0648\u0644 \u0623\u0646 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0630\u0627\u062a \u0627\u0644\u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u0627\u0644\u0623\u0643\u0628\u0631 \u0645\u0646 \u0623\u0648 \u062a\u0633\u0627\u0648\u064a 0.5 \u0633\u064a\u062a\u0645 \u062a\u0635\u0646\u064a\u0641\u0647\u0627 \u0639\u0644\u0649 \u0623\u0646\u0647\u0627 &#8220;1&#8221; \u0648\u0633\u064a\u062a\u0645 \u062a\u0635\u0646\u064a\u0641 \u062c\u0645\u064a\u0639 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0627\u0644\u0623\u062e\u0631\u0649 \u0639\u0644\u0649 \u0623\u0646\u0647\u0627 &#8220;0&#8221;.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0641\u0631 \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0645\u062b\u0627\u0644\u0627\u064b \u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629 \u0644\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0641\u064a R.<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 1: \u0627\u0633\u062a\u064a\u0631\u0627\u062f \u0627\u0644\u062d\u0632\u0645 \u0627\u0644\u0644\u0627\u0632\u0645\u0629<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u0627\u0633\u062a\u064a\u0631\u0627\u062f \u0627\u0644\u062d\u0632\u0645 \u0627\u0644\u0644\u0627\u0632\u0645\u0629 \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/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<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LogisticRegression\n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> metrics\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<\/strong><\/pre>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 2: \u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a <strong>\u0627\u0644\u0627\u0641\u062a\u0631\u0627\u0636\u064a\u0629<\/strong> \u0645\u0646 <a href=\"https:\/\/www.ime.unicamp.br\/~dias\/Intoduction%20to%20Statistical%20Learning.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">\u0643\u062a\u0627\u0628 \u0645\u0642\u062f\u0645\u0629 \u0625\u0644\u0649 \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0625\u062d\u0635\u0627\u0626\u064a<\/a> . \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0644\u062a\u062d\u0645\u064a\u0644 \u0648\u0639\u0631\u0636 \u0645\u0644\u062e\u0635 \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#import dataset from CSV file on Github\n<span style=\"color: #000000;\">url = \"https:\/\/raw.githubusercontent.com\/- Statorials\/Python-Guides\/main\/default.csv\"\ndata = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n<\/span><\/span>\n<span style=\"color: #008080;\">#view first six rows of dataset\n<\/span>data[0:6]\n\n        default student balance income\n0 0 0 729.526495 44361.625074\n1 0 1 817.180407 12106.134700\n2 0 0 1073.549164 31767.138947\n3 0 0 529.250605 35704.493935\n4 0 0 785.655883 38463.495879\n5 0 1 919.588530 7491.558572  \n\n<span style=\"color: #008080;\">#find total observations in dataset<\/span>\nlen( <span style=\"color: #3366ff;\">data.index<\/span> )\n\n10000\n<\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u062d\u062a\u0648\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0647\u0630\u0647 \u0639\u0644\u0649 \u0627\u0644\u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0639\u0646 10000 \u0641\u0631\u062f:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0627\u0641\u062a\u0631\u0627\u0636\u064a:<\/strong> \u064a\u0634\u064a\u0631 \u0625\u0644\u0649 \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0627\u0644\u0641\u0631\u062f \u0642\u062f \u062a\u062e\u0644\u0641 \u0623\u0645 \u0644\u0627.<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0637\u0627\u0644\u0628:<\/strong> \u064a\u0634\u064a\u0631 \u0625\u0644\u0649 \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0627\u0644\u0641\u0631\u062f \u0637\u0627\u0644\u0628\u064b\u0627 \u0623\u0645 \u0644\u0627.<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u0631\u0635\u064a\u062f:<\/strong> \u0645\u062a\u0648\u0633\u0637 \u0627\u0644\u0631\u0635\u064a\u062f \u0627\u0644\u0630\u064a \u064a\u062d\u0645\u0644\u0647 \u0627\u0644\u0641\u0631\u062f.<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062f\u062e\u0644:<\/strong> \u062f\u062e\u0644 \u0627\u0644\u0641\u0631\u062f.<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u062d\u0627\u0644\u0629 \u0627\u0644\u0637\u0627\u0644\u0628\u060c \u0648\u0627\u0644\u0631\u0635\u064a\u062f \u0627\u0644\u0628\u0646\u0643\u064a\u060c \u0648\u0627\u0644\u062f\u062e\u0644 \u0644\u0628\u0646\u0627\u0621 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0627\u0644\u0630\u064a \u064a\u062a\u0646\u0628\u0623 \u0628\u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u062a\u062e\u0644\u0641 \u0641\u0631\u062f \u0645\u0639\u064a\u0646 \u0639\u0646 \u0627\u0644\u0633\u062f\u0627\u062f.<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 3: \u0625\u0646\u0634\u0627\u0621 \u0639\u064a\u0646\u0627\u062a \u0627\u0644\u062a\u062f\u0631\u064a\u0628 \u0648\u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u062a\u0642\u0633\u064a\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u062a\u062f\u0631\u064a\u0628 <em>\u0644\u062a\u062f\u0631\u064a\u0628<\/em> \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0639\u0644\u064a\u0647\u0627 \u0648\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u062e\u062a\u0628\u0627\u0631 <em>\u0644\u0627\u062e\u062a\u0628\u0627\u0631<\/em> \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0639\u0644\u064a\u0647\u0627.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define the predictor variables and the response variable\n<\/span>X = data[[' <span style=\"color: #008000;\">student<\/span> ',' <span style=\"color: #008000;\">balance<\/span> ',' <span style=\"color: #008000;\">income<\/span> ']]\ny = data[' <span style=\"color: #008000;\">default<\/span> ']\n\n<span style=\"color: #008080;\">#split the dataset into training (70%) and testing (30%) sets\n<\/span>X_train,X_test,y_train,y_test = <span style=\"color: #3366ff;\">train_test_split<\/span> (X,y,test_size=0.3,random_state=0)<\/strong><\/pre>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 4: \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0646\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u062f\u0627\u0644\u0629 <b>LogisticRegression()<\/b> \u200e \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0644\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#instantiate the model\n<\/span>log_regression = LogisticRegression()\n\n<span style=\"color: #008080;\">#fit the model using the training data\n<\/span>log_regression. <span style=\"color: #3366ff;\">fit<\/span> (X_train,y_train)\n\n<span style=\"color: #008080;\">#use model to make predictions on test data\n<\/span>y_pred = log_regression. <span style=\"color: #3366ff;\">predict<\/span> (X_test)\n<\/strong><\/pre>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 5: \u062a\u0634\u062e\u064a\u0635 \u0627\u0644\u0646\u0645\u0627\u0630\u062c<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0628\u0645\u062c\u0631\u062f \u062a\u0631\u0643\u064a\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0628\u0639\u062f \u0630\u0644\u0643 \u062a\u062d\u0644\u064a\u0644 \u0623\u062f\u0627\u0621 \u0646\u0645\u0648\u0630\u062c\u0646\u0627 \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u0625\u0646\u0634\u0627\u0621 \u0645\u0635\u0641\u0648\u0641\u0629 \u0627\u0644\u0627\u0631\u062a\u0628\u0627\u0643<\/span> <span style=\"color: #000000;\">\u0644\u0644\u0646\u0645\u0648\u0630\u062c:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong>cnf_matrix = metrics. <span style=\"color: #3366ff;\">confusion_matrix<\/span> (y_test, y_pred)\ncnf_matrix\n\narray([[2886, 1],\n       [113,0]])\n<\/strong><\/span><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u0646 \u0645\u0635\u0641\u0648\u0641\u0629 \u0627\u0644\u0627\u0631\u062a\u0628\u0627\u0643 \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">#\u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629: 2886<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">#\u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0633\u0644\u0628\u064a\u0629 \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629: 0<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">#\u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629 \u0627\u0644\u0643\u0627\u0630\u0628\u0629: 113<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">#\u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0633\u0644\u0628\u064a\u0629 \u0627\u0644\u0643\u0627\u0630\u0628\u0629: 1<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0627\u0644\u062d\u0635\u0648\u0644 \u0639\u0644\u0649 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u062f\u0642\u0629 \u0627\u0644\u0630\u064a \u064a\u062e\u0628\u0631\u0646\u0627 \u0628\u0646\u0633\u0628\u0629 \u062a\u0646\u0628\u0624\u0627\u062a \u0627\u0644\u062a\u0635\u062d\u064a\u062d \u0627\u0644\u062a\u064a \u0623\u062c\u0631\u0627\u0647\u0627 \u0627\u0644\u0646\u0645\u0648\u0630\u062c:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>print(\" <span style=\"color: #008000;\">Accuracy:<\/span> \", <span style=\"color: #3366ff;\">metrics.accuracy_score<\/span> (y_test, y_pred))l\n\nAccuracy: 0.962\n<\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u062e\u0628\u0631\u0646\u0627 \u0647\u0630\u0627 \u0623\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0642\u0627\u0645 \u0628\u0627\u0644\u062a\u0646\u0628\u0624 \u0627\u0644\u0635\u062d\u064a\u062d \u062d\u0648\u0644 \u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0627\u0644\u0641\u0631\u062f \u0633\u064a\u062a\u062e\u0644\u0641 \u0639\u0646 \u0627\u0644\u0633\u062f\u0627\u062f <strong>\u0628\u0646\u0633\u0628\u0629 96.2%<\/strong> \u0645\u0646 \u0627\u0644\u0648\u0642\u062a \u0623\u0645 \u0644\u0627.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0623\u062e\u064a\u0631\u064b\u0627\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0631\u0633\u0645 \u0645\u0646\u062d\u0646\u0649 \u062e\u0627\u0635\u064a\u0629 \u062a\u0634\u063a\u064a\u0644 \u0627\u0644\u0645\u0633\u062a\u0642\u0628\u0644 (ROC) \u0627\u0644\u0630\u064a \u064a\u0639\u0631\u0636 \u0627\u0644\u0646\u0633\u0628\u0629 \u0627\u0644\u0645\u0626\u0648\u064a\u0629 \u0644\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0627\u062a \u0627\u0644\u062d\u0642\u064a\u0642\u064a\u0629 \u0627\u0644\u062a\u064a \u062a\u0646\u0628\u0623 \u0628\u0647\u0627 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0639\u0646\u062f\u0645\u0627 \u064a\u062a\u0645 \u062a\u062e\u0641\u064a\u0636 \u0639\u062a\u0628\u0629 \u0627\u062d\u062a\u0645\u0627\u0644 \u0627\u0644\u062a\u0646\u0628\u0624 \u0645\u0646 1 \u0625\u0644\u0649 0.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0643\u0644\u0645\u0627 \u0627\u0631\u062a\u0641\u0639\u062a \u0627\u0644\u0645\u0633\u0627\u062d\u0629 \u062a\u062d\u062a \u0627\u0644\u0645\u0646\u062d\u0646\u0649 (AUC)\u060c \u0632\u0627\u062f\u062a \u062f\u0642\u0629 \u0646\u0645\u0648\u0630\u062c\u0646\u0627 \u0641\u064a \u0627\u0644\u062a\u0646\u0628\u0624 \u0628\u0627\u0644\u0646\u062a\u0627\u0626\u062c:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics<\/span>\ny_pred_proba = log_regression. <span style=\"color: #3366ff;\">predict_proba<\/span> (X_test)[::,1]\nfpr, tpr, _ = metrics. <span style=\"color: #3366ff;\">roc_curve<\/span> (y_test, y_pred_proba)\nauc = metrics. <span style=\"color: #3366ff;\">roc_auc_score<\/span> (y_test, y_pred_proba)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr,label=\" <span style=\"color: #008000;\">AUC=<\/span> \"+str(auc))\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=4)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/span><\/pre>\n<p style=\";text-align:right;direction:rtl\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11591 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/auc1.png\" alt=\"\u0645\u0646\u062d\u0646\u0649 ROC \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\" width=\"389\" height=\"262\" srcset=\"\" sizes=\"\"><\/p>\n<div class=\"entry-content entry-content-single\" data-content-ads-inserted=\"true\" style=\";text-align:right;direction:rtl\">\n<p style=\";text-align:right;direction:rtl\"> <em><span style=\"color: #000000;\">\u064a\u0645\u0643\u0646 \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0643\u0648\u062f Python \u0627\u0644\u0643\u0627\u0645\u0644 \u0627\u0644\u0645\u0633\u062a\u062e\u062f\u0645 \u0641\u064a \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a <a href=\"https:\/\/github.com\/- Statorials\/Python-Guides\/blob\/main\/logistic_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u0647\u0646\u0627<\/a> .<\/span><\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0647\u0648 \u0637\u0631\u064a\u0642\u0629 \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627 \u0644\u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0639\u0646\u062f\u0645\u0627 \u064a\u0643\u0648\u0646 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u062b\u0646\u0627\u0626\u064a\u064b\u0627. \u064a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0637\u0631\u064a\u0642\u0629 \u062a\u0639\u0631\u0641 \u0628\u062a\u0642\u062f\u064a\u0631 \u0627\u0644\u0627\u062d\u062a\u0645\u0627\u0644\u064a\u0629 \u0627\u0644\u0642\u0635\u0648\u0649 \u0644\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0645\u0639\u0627\u062f\u0644\u0629 \u0628\u0627\u0644\u0634\u0643\u0644 \u0627\u0644\u062a\u0627\u0644\u064a: \u0633\u062c\u0644[p(X) \/ ( 1 -p(X))] = \u03b2 0 + \u03b2 1 X 1 + \u03b2 2 X 2 + \u2026 + \u03b2 p \u0630\u0647\u0628: X j : \u0627\u0644\u0645\u062a\u063a\u064a\u0631 [&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":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 (\u062e\u0637\u0648\u0629 \u0628\u062e\u0637\u0648\u0629) - \u0639\u0644\u0645 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