{"id":1155,"date":"2023-07-27T11:31:28","date_gmt":"2023-07-27T11:31:28","guid":{"rendered":"https:\/\/statorials.org\/hi\/%e0%a4%b2%e0%a5%89%e0%a4%9c%e0%a4%bf%e0%a4%b8%e0%a5%8d%e0%a4%9f%e0%a4%bf%e0%a4%95-%e0%a4%b0%e0%a4%bf%e0%a4%97%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%b6%e0%a4%a8-%e0%a4%aa%e0%a4%be%e0%a4%af%e0%a4%a5%e0%a4%a8\/"},"modified":"2023-07-27T11:31:28","modified_gmt":"2023-07-27T11:31:28","slug":"%e0%a4%b2%e0%a5%89%e0%a4%9c%e0%a4%bf%e0%a4%b8%e0%a5%8d%e0%a4%9f%e0%a4%bf%e0%a4%95-%e0%a4%b0%e0%a4%bf%e0%a4%97%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%b6%e0%a4%a8-%e0%a4%aa%e0%a4%be%e0%a4%af%e0%a4%a5%e0%a4%a8","status":"publish","type":"post","link":"https:\/\/statorials.org\/hi\/%e0%a4%b2%e0%a5%89%e0%a4%9c%e0%a4%bf%e0%a4%b8%e0%a5%8d%e0%a4%9f%e0%a4%bf%e0%a4%95-%e0%a4%b0%e0%a4%bf%e0%a4%97%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%b6%e0%a4%a8-%e0%a4%aa%e0%a4%be%e0%a4%af%e0%a4%a5%e0%a4%a8\/","title":{"rendered":"\u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902 (\u091a\u0930\u0923 \u0926\u0930 \u091a\u0930\u0923)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/hi\/\u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915-\u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928<\/a> \u090f\u0915 \u0910\u0938\u0940 \u0935\u093f\u0927\u093f \u0939\u0948 \u091c\u093f\u0938\u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0939\u092e \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u091c\u092c <a href=\"https:\/\/statorials.org\/hi\/\u091a\u0930-\u0935\u094d\u092f\u093e\u0916\u094d\u092f\u093e\u0924\u094d\u092e\u0915-\u092a\u094d\u0930\u0924\u093f\u0915\u094d\u0930\u093f\u092f\u093e\u090f\u0901\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u092a\u094d\u0930\u0924\u093f\u0915\u094d\u0930\u093f\u092f\u093e \u091a\u0930<\/a> \u0926\u094d\u0935\u093f\u0906\u0927\u093e\u0930\u0940 \u0939\u094b\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0930\u0942\u092a \u0915\u093e \u0938\u092e\u0940\u0915\u0930\u0923 \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f <em>\u0905\u0927\u093f\u0915\u0924\u092e \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0905\u0928\u0941\u092e\u093e\u0928<\/em> \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 \u091c\u094d\u091e\u093e\u0924 \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0924\u093e \u0939\u0948:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0932\u0949\u0917[\u092a\u0940(\u090f\u0915\u094d\u0938) \/ ( <sub>1<\/sub> -\u092a\u0940(\u090f\u0915\u094d\u0938))] = \u03b2 <sub>0<\/sub> + \u03b2 <sub>1<\/sub> \u090f\u0915\u094d\u0938 <sub>1<\/sub> + \u03b2 <sub>2<\/sub> \u090f\u0915\u094d\u0938 <sub>2<\/sub> + \u2026 + \u03b2 <sub>\u092a\u0940<\/sub><\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0938\u094b\u0928\u093e:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u090f\u0915\u094d\u0938 <sub>\u091c\u0947<\/sub><\/strong> : \u091c\u0947 <sup>\u0935\u0947\u0902<\/sup> \u092a\u0942\u0930\u094d\u0935\u093e\u0928\u0941\u092e\u093e\u0928\u093f\u0924 \u091a\u0930<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u03b2 <sub>j<\/sub><\/strong> : j <sup>\u0935\u0947\u0902<\/sup> \u092a\u0942\u0930\u094d\u0935\u093e\u0928\u0941\u092e\u093e\u0928\u093f\u0924 \u091a\u0930 \u0915\u0947 \u0932\u093f\u090f \u0917\u0941\u0923\u093e\u0902\u0915 \u0915\u093e \u0905\u0928\u0941\u092e\u093e\u0928<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0938\u092e\u0940\u0915\u0930\u0923 \u0915\u0947 \u0926\u093e\u0908\u0902 \u0913\u0930 \u0915\u093e \u0938\u0942\u0924\u094d\u0930 <strong>\u0932\u0949\u0917 \u0911\u0921\u094d\u0938 \u0915\u0940<\/strong> \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f \u092a\u094d\u0930\u0924\u093f\u0915\u094d\u0930\u093f\u092f\u093e \u091a\u0930 \u092e\u093e\u0928 1 \u0932\u0947\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0932\u093f\u090f, \u091c\u092c \u0939\u092e \u090f\u0915 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0924\u0947 \u0939\u0948\u0902, \u0924\u094b \u0939\u092e \u0907\u0938 \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0938\u092e\u0940\u0915\u0930\u0923 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f \u0915\u093f\u0938\u0940 \u0926\u093f\u090f \u0917\u090f \u0905\u0935\u0932\u094b\u0915\u0928 \u0915\u093e \u092e\u093e\u0928 1 \u0939\u0948:<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092a\u0940(\u090f\u0915\u094d\u0938) = \u0908 <sup>\u03b2 <sub>0<\/sub> + <sub>\u03b2<\/sub> <sub>1<\/sub> <sub>\u090f\u0915\u094d\u0938<\/sub> <sub>1<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> <sup><sub>2<\/sub> <sub>\u090f\u0915\u094d\u0938<\/sub> <sub>2<\/sub> <sub>+<\/sub> <sub>\u2026<\/sub> <sub>+<\/sub> <sub>\u03b2<\/sub><\/sup> \u092a\u0940<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092b\u093f\u0930 \u0939\u092e \u0905\u0935\u0932\u094b\u0915\u0928 \u0915\u094b 1 \u092f\u093e 0 \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 \u0935\u0930\u094d\u0917\u0940\u0915\u0943\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u090f\u0915 \u0928\u093f\u0936\u094d\u091a\u093f\u0924 \u0938\u0902\u092d\u093e\u0935\u094d\u092f\u0924\u093e \u0938\u0940\u092e\u093e \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e \u0915\u0939 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f 0.5 \u0938\u0947 \u0905\u0927\u093f\u0915 \u092f\u093e \u0909\u0938\u0915\u0947 \u092c\u0930\u093e\u092c\u0930 \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0935\u093e\u0932\u0947 \u0905\u0935\u0932\u094b\u0915\u0928\u094b\u0902 \u0915\u094b &#8220;1&#8221; \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 \u0935\u0930\u094d\u0917\u0940\u0915\u0943\u0924 \u0915\u093f\u092f\u093e \u091c\u093e\u090f\u0917\u093e \u0914\u0930 \u0905\u0928\u094d\u092f \u0938\u092d\u0940 \u0905\u0935\u0932\u094b\u0915\u0928\u094b\u0902 \u0915\u094b &#8220;0&#8221; \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 \u0935\u0930\u094d\u0917\u0940\u0915\u0943\u0924 \u0915\u093f\u092f\u093e \u091c\u093e\u090f\u0917\u093e\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u0906\u0930 \u092e\u0947\u0902 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0948\u0938\u0947 \u0915\u0930\u0947\u0902 \u0907\u0938\u0915\u093e \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u092a\u094d\u0930\u0926\u093e\u0928 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 1: \u0906\u0935\u0936\u094d\u092f\u0915 \u092a\u0948\u0915\u0947\u091c \u0906\u092f\u093e\u0924 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0938\u092c\u0938\u0947 \u092a\u0939\u0932\u0947, \u0939\u092e \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0906\u0935\u0936\u094d\u092f\u0915 \u092a\u0948\u0915\u0947\u091c \u0906\u092f\u093e\u0924 \u0915\u0930\u0947\u0902\u0917\u0947:<\/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> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 2: \u0921\u0947\u091f\u093e \u0932\u094b\u0921 \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0907\u0938 \u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e <a href=\"https:\/\/www.ime.unicamp.br\/~dias\/Intoduction%20to%20Statistical%20Learning.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">\u0907\u0902\u091f\u094d\u0930\u094b\u0921\u0915\u094d\u0936\u0928 \u091f\u0942 \u0938\u094d\u091f\u0948\u091f\u093f\u0938\u094d\u091f\u093f\u0915\u0932 \u0932\u0930\u094d\u0928\u093f\u0902\u0917 \u092a\u0941\u0938\u094d\u0924\u0915<\/a> \u0938\u0947 <strong>\u0921\u093f\u092b\u093c\u0949\u0932\u094d\u091f<\/strong> \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902\u0917\u0947\u0964 \u0939\u092e \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u093e \u0938\u093e\u0930\u093e\u0902\u0936 \u0932\u094b\u0921 \u0915\u0930\u0928\u0947 \u0914\u0930 \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u093f\u0924 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0915\u094b\u0921 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902:<\/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> <span style=\"color: #000000;\">\u0907\u0938 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 10,000 \u0935\u094d\u092f\u0915\u094d\u0924\u093f\u092f\u094b\u0902 \u0915\u0947 \u092c\u093e\u0930\u0947 \u092e\u0947\u0902 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u091c\u093e\u0928\u0915\u093e\u0930\u0940 \u0936\u093e\u092e\u093f\u0932 \u0939\u0948:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u0921\u093f\u092b\u093c\u0949\u0932\u094d\u091f:<\/strong> \u0907\u0902\u0917\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u093f\u0938\u0940 \u0935\u094d\u092f\u0915\u094d\u0924\u093f \u0928\u0947 \u0921\u093f\u092b\u093c\u0949\u0932\u094d\u091f \u0915\u093f\u092f\u093e \u0939\u0948 \u092f\u093e \u0928\u0939\u0940\u0902\u0964<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u091b\u093e\u0924\u094d\u0930:<\/strong> \u0907\u0902\u0917\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u094b\u0908 \u0935\u094d\u092f\u0915\u094d\u0924\u093f \u091b\u093e\u0924\u094d\u0930 \u0939\u0948 \u092f\u093e \u0928\u0939\u0940\u0902\u0964<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0936\u0947\u0937:<\/strong> \u0915\u093f\u0938\u0940 \u0935\u094d\u092f\u0915\u094d\u0924\u093f \u0926\u094d\u0935\u093e\u0930\u093e \u0930\u0916\u093e \u0917\u092f\u093e \u0914\u0938\u0924 \u0936\u0947\u0937\u0964<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0906\u092f:<\/strong> \u0935\u094d\u092f\u0915\u094d\u0924\u093f \u0915\u0940 \u0906\u092f.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0939\u092e \u090f\u0915 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u092c\u0928\u093e\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u091b\u093e\u0924\u094d\u0930 \u0915\u0940 \u0938\u094d\u0925\u093f\u0924\u093f, \u092c\u0948\u0902\u0915 \u092c\u0948\u0932\u0947\u0902\u0938 \u0914\u0930 \u0906\u092f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902\u0917\u0947 \u091c\u094b \u0907\u0938 \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0915\u0940 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f \u0915\u094b\u0908 \u0935\u094d\u092f\u0915\u094d\u0924\u093f \u0921\u093f\u092b\u093c\u0949\u0932\u094d\u091f \u0939\u094b\u0917\u093e\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 3: \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0914\u0930 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0928\u092e\u0942\u0928\u0947 \u092c\u0928\u093e\u090f\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0915\u0947 \u092c\u093e\u0926, \u0939\u092e \u092e\u0949\u0921\u0932 \u0915\u094b <em>\u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u093f\u0924 \u0915\u0930\u0928\u0947<\/em> \u0915\u0947 \u0932\u093f\u090f \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0915\u094b \u090f\u0915 \u092a\u094d\u0930\u0936\u093f\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u0914\u0930 \u092e\u0949\u0921\u0932 \u0915\u093e <em>\u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f<\/em> \u090f\u0915 \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0938\u0947\u091f \u092e\u0947\u0902 \u0935\u093f\u092d\u093e\u091c\u093f\u0924 \u0915\u0930\u0947\u0902\u0917\u0947\u0964<\/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> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 4: \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0915\u0947 \u092c\u093e\u0926, \u0939\u092e \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092e\u0947\u0902 \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f <b>\u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928()<\/b> \u092b\u093c\u0902\u0915\u094d\u0936\u0928 \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0947\u0902\u0917\u0947:<\/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> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 5: \u092e\u0949\u0921\u0932 \u0921\u093e\u092f\u0917\u094d\u0928\u094b\u0938\u094d\u091f\u093f\u0915\u094d\u0938<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u090f\u0915 \u092c\u093e\u0930 \u091c\u092c \u0939\u092e \u092a\u094d\u0930\u0924\u093f\u0917\u092e\u0928 \u092e\u0949\u0921\u0932 \u092b\u093f\u091f \u0915\u0930 \u0932\u0947\u0924\u0947 \u0939\u0948\u0902, \u0924\u094b \u0939\u092e \u092a\u0930\u0940\u0915\u094d\u0937\u0923 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u092a\u0930 \u0905\u092a\u0928\u0947 \u092e\u0949\u0921\u0932 \u0915\u0947 \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u0928 \u0915\u093e \u0935\u093f\u0936\u094d\u0932\u0947\u0937\u0923 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902\u0964<\/span><\/p>\n<p> \u0938\u092c\u0938\u0947 \u092a\u0939\u0932\u0947, \u0939\u092e <span style=\"color: #000000;\">\u092e\u0949\u0921\u0932 \u0915\u0947 \u0932\u093f\u090f<\/span> <span style=\"color: #000000;\">\u092d\u094d\u0930\u092e \u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u092c\u0928\u093e\u090f\u0902\u0917\u0947<\/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> <span style=\"color: #000000;\">\u092d\u094d\u0930\u092e \u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u0938\u0947 \u0939\u092e \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u0915\u093f:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">#\u0938\u091a\u094d\u091a\u0940 \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u093f\u092f\u093e\u0901: 2886<\/span><\/li>\n<li> <span style=\"color: #000000;\">#\u0938\u091a\u094d\u091a\u0940 \u0928\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u093f\u092f\u093e\u0901: 0<\/span><\/li>\n<li> <span style=\"color: #000000;\">#\u091d\u0942\u0920\u0940 \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u093f\u092f\u093e\u0901: 113<\/span><\/li>\n<li> <span style=\"color: #000000;\">#\u0917\u0932\u0924 \u0928\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u093f\u092f\u093e\u0901: 1<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0939\u092e \u0938\u091f\u0940\u0915\u0924\u093e \u092e\u0949\u0921\u0932 \u092d\u0940 \u092a\u094d\u0930\u093e\u092a\u094d\u0924 \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902, \u091c\u094b \u0939\u092e\u0947\u0902 \u092e\u0949\u0921\u0932 \u0926\u094d\u0935\u093e\u0930\u093e \u0915\u093f\u090f \u0917\u090f \u0938\u0941\u0927\u093e\u0930 \u092a\u0942\u0930\u094d\u0935\u093e\u0928\u0941\u092e\u093e\u0928\u094b\u0902 \u0915\u093e \u092a\u094d\u0930\u0924\u093f\u0936\u0924 \u092c\u0924\u093e\u0924\u093e \u0939\u0948:<\/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> <span style=\"color: #000000;\">\u092f\u0939 \u0939\u092e\u0947\u0902 \u092c\u0924\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u092e\u0949\u0921\u0932 \u0928\u0947 \u0907\u0938 \u092c\u093e\u0930\u0947 \u092e\u0947\u0902 \u0938\u0939\u0940 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0940 \u0939\u0948 \u0915\u093f \u0915\u094b\u0908 \u0935\u094d\u092f\u0915\u094d\u0924\u093f <strong>96.2%<\/strong> \u0938\u092e\u092f \u0921\u093f\u092b\u0949\u0932\u094d\u091f \u0915\u0930\u0947\u0917\u093e \u092f\u093e \u0928\u0939\u0940\u0902\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0905\u0902\u0924 \u092e\u0947\u0902, \u0939\u092e \u0930\u093f\u0938\u0940\u0935\u0930 \u0911\u092a\u0930\u0947\u091f\u093f\u0902\u0917 \u0915\u0948\u0930\u0947\u0915\u094d\u091f\u0930\u093f\u0938\u094d\u091f\u093f\u0915 (\u0906\u0930\u0913\u0938\u0940) \u0935\u0915\u094d\u0930 \u0915\u094b \u092a\u094d\u0932\u0949\u091f \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u091c\u094b \u092e\u0949\u0921\u0932 \u0926\u094d\u0935\u093e\u0930\u093e \u0905\u0928\u0941\u092e\u093e\u0928\u093f\u0924 \u0935\u093e\u0938\u094d\u0924\u0935\u093f\u0915 \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915\u0924\u093e \u0915\u093e \u092a\u094d\u0930\u0924\u093f\u0936\u0924 \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948 \u091c\u092c \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0938\u0940\u092e\u093e 1 \u0938\u0947 0 \u0924\u0915 \u0915\u092e \u0939\u094b \u091c\u093e\u0924\u0940 \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC (\u0935\u0915\u094d\u0930 \u0915\u0947 \u0928\u0940\u091a\u0947 \u0915\u093e \u0915\u094d\u0937\u0947\u0924\u094d\u0930) \u091c\u093f\u0924\u0928\u093e \u0905\u0927\u093f\u0915 \u0939\u094b\u0917\u093e, \u0939\u092e\u093e\u0930\u093e \u092e\u0949\u0921\u0932 \u0909\u0924\u0928\u093e \u0939\u0940 \u0938\u091f\u0940\u0915 \u0930\u0942\u092a \u0938\u0947 \u092a\u0930\u093f\u0923\u093e\u092e\u094b\u0902 \u0915\u0940 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0930\u0928\u0947 \u092e\u0947\u0902 \u0938\u0915\u094d\u0937\u092e \u0939\u094b\u0917\u093e:<\/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><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11591 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/auc1.png\" alt=\"\u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930\" width=\"389\" height=\"262\" srcset=\"\" sizes=\"auto, \"><\/p>\n<div class=\"entry-content entry-content-single\" data-content-ads-inserted=\"true\">\n<p> <em><span style=\"color: #000000;\">\u0907\u0938 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u092e\u0947\u0902 \u0909\u092a\u092f\u094b\u0917 \u0915\u093f\u092f\u093e \u0917\u092f\u093e \u092a\u0942\u0930\u093e \u092a\u093e\u092f\u0925\u0928 \u0915\u094b\u0921 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/logistic_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u092f\u0939\u093e\u0902<\/a> \u092a\u093e\u092f\u093e \u091c\u093e \u0938\u0915\u0924\u093e \u0939\u0948\u0964<\/span><\/em><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>\u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u090f\u0915 \u0910\u0938\u0940 \u0935\u093f\u0927\u093f \u0939\u0948 \u091c\u093f\u0938\u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0939\u092e \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0915\u094b \u092b\u093f\u091f \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u091c\u092c \u092a\u094d\u0930\u0924\u093f\u0915\u094d\u0930\u093f\u092f\u093e \u091a\u0930 \u0926\u094d\u0935\u093f\u0906\u0927\u093e\u0930\u0940 \u0939\u094b\u0924\u093e \u0939\u0948\u0964 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0930\u0942\u092a \u0915\u093e \u0938\u092e\u0940\u0915\u0930\u0923 \u0916\u094b\u091c\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0905\u0927\u093f\u0915\u0924\u092e \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0905\u0928\u0941\u092e\u093e\u0928 \u0915\u0947 \u0930\u0942\u092a \u092e\u0947\u0902 \u091c\u094d\u091e\u093e\u0924 \u0935\u093f\u0927\u093f \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0924\u093e \u0939\u0948: \u0932\u0949\u0917[\u092a\u0940(\u090f\u0915\u094d\u0938) \/ ( 1 -\u092a\u0940(\u090f\u0915\u094d\u0938))] = \u03b2 0 + [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-1155","post","type-post","status-publish","format-standard","hentry","category-3"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 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