{"id":1584,"date":"2023-07-25T18:37:40","date_gmt":"2023-07-25T18:37:40","guid":{"rendered":"https:\/\/statorials.org\/hi\/%e0%a4%9f%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%b8-%e0%a4%b0%e0%a5%89%e0%a4%95-%e0%a4%95%e0%a4%b0%e0%a5%8d%e0%a4%b5-%e0%a4%aa%e0%a4%be%e0%a4%af%e0%a4%a5%e0%a4%a8\/"},"modified":"2023-07-25T18:37:40","modified_gmt":"2023-07-25T18:37:40","slug":"%e0%a4%9f%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%b8-%e0%a4%b0%e0%a5%89%e0%a4%95-%e0%a4%95%e0%a4%b0%e0%a5%8d%e0%a4%b5-%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%9f%e0%a5%8d%e0%a4%b0%e0%a5%87%e0%a4%b8-%e0%a4%b0%e0%a5%89%e0%a4%95-%e0%a4%95%e0%a4%b0%e0%a5%8d%e0%a4%b5-%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 \u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930 \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u090f\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 \u0938\u093e\u0902\u0916\u094d\u092f\u093f\u0915\u0940\u092f \u092a\u0926\u094d\u0927\u0924\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\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 \u092f\u0939 \u092e\u0942\u0932\u094d\u092f\u093e\u0902\u0915\u0928 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u093f \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0921\u0947\u091f\u093e \u0938\u0947\u091f \u092e\u0947\u0902 \u0915\u093f\u0924\u0928\u0940 \u0905\u091a\u094d\u091b\u0940 \u0924\u0930\u0939 \u092b\u093f\u091f \u092c\u0948\u0920\u0924\u093e \u0939\u0948, \u0939\u092e \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0926\u094b \u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>\u0938\u0902\u0935\u0947\u0926\u0928\u0936\u0940\u0932\u0924\u093e:<\/strong> \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0939\u0948 \u0915\u093f \u092e\u0949\u0921\u0932 \u0915\u093f\u0938\u0940 \u0905\u0935\u0932\u094b\u0915\u0928 \u0915\u0947 \u0932\u093f\u090f \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u092a\u0930\u093f\u0923\u093e\u092e \u0915\u0940 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0930\u0924\u093e \u0939\u0948 \u091c\u092c \u092a\u0930\u093f\u0923\u093e\u092e \u0935\u093e\u0938\u094d\u0924\u0935 \u092e\u0947\u0902 \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u0939\u094b\u0924\u093e \u0939\u0948\u0964 \u0907\u0938\u0947 &#8220;\u0938\u091a\u094d\u091a\u0940 \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u0926\u0930&#8221; \u092d\u0940 \u0915\u0939\u093e \u091c\u093e\u0924\u093e \u0939\u0948\u0964<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>\u0935\u093f\u0936\u093f\u0937\u094d\u091f\u0924\u093e:<\/strong> \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0939\u0948 \u0915\u093f \u092e\u0949\u0921\u0932 \u0915\u093f\u0938\u0940 \u0905\u0935\u0932\u094b\u0915\u0928 \u0915\u0947 \u0932\u093f\u090f \u0928\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u092a\u0930\u093f\u0923\u093e\u092e \u0915\u0940 \u092d\u0935\u093f\u0937\u094d\u092f\u0935\u093e\u0923\u0940 \u0915\u0930\u0924\u093e \u0939\u0948 \u091c\u092c \u092a\u0930\u093f\u0923\u093e\u092e \u0935\u093e\u0938\u094d\u0924\u0935 \u092e\u0947\u0902 \u0928\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u0939\u094b\u0924\u093e \u0939\u0948\u0964 \u0907\u0938\u0947 &#8220;\u0938\u091a\u094d\u091a\u0940 \u0928\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u0926\u0930&#8221; \u092d\u0940 \u0915\u0939\u093e \u091c\u093e\u0924\u093e \u0939\u0948\u0964<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0907\u0928 \u0926\u094b \u092e\u093e\u092a\u094b\u0902 \u0915\u094b \u0926\u0947\u0916\u0928\u0947 \u0915\u093e \u090f\u0915 \u0924\u0930\u0940\u0915\u093e \u090f\u0915 <strong>\u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930<\/strong> \u092c\u0928\u093e\u0928\u093e \u0939\u0948, \u091c\u094b &#8220;\u0930\u093f\u0938\u0940\u0935\u0930 \u0911\u092a\u0930\u0947\u091f\u093f\u0902\u0917 \u0935\u093f\u0936\u0947\u0937\u0924\u093e&#8221; \u0935\u0915\u094d\u0930 \u0915\u0947 \u0932\u093f\u090f \u0939\u0948\u0964 \u092f\u0939 \u090f\u0915 \u0917\u094d\u0930\u093e\u092b\u093c \u0939\u0948 \u091c\u094b \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0915\u0940 \u0938\u0902\u0935\u0947\u0926\u0928\u0936\u0940\u0932\u0924\u093e \u0914\u0930 \u0935\u093f\u0936\u093f\u0937\u094d\u091f\u0924\u093e \u0915\u094b \u092a\u094d\u0930\u0926\u0930\u094d\u0936\u093f\u0924 \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u0926\u093f\u0916\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930 \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u092f\u093e \u0914\u0930 \u0935\u094d\u092f\u093e\u0916\u094d\u092f\u093e \u0915\u093f\u092f\u093e \u091c\u093e\u090f\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong><span style=\"color: #000000;\">\u091a\u0930\u0923 1: \u0906\u0935\u0936\u094d\u092f\u0915 \u092a\u0948\u0915\u0947\u091c \u0906\u092f\u093e\u0924 \u0915\u0930\u0947\u0902<\/span><\/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: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">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: \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 \u090f\u0915 \u0921\u0947\u091f\u093e\u0938\u0947\u091f \u0906\u092f\u093e\u0924 \u0915\u0930\u0947\u0902\u0917\u0947 \u0914\u0930 \u0909\u0938\u092e\u0947\u0902 \u090f\u0915 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u092b\u093f\u091f \u0915\u0930\u0947\u0902\u0917\u0947:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#import dataset from CSV file on Github\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/default.csv\"\ndata = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#define the predictor variables and the response variable\n<\/span>X = data[[' <span style=\"color: #ff0000;\">student<\/span> ',' <span style=\"color: #ff0000;\">balance<\/span> ',' <span style=\"color: #ff0000;\">income<\/span> ']]\ny = data[' <span style=\"color: #ff0000;\">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 = train_test_split(X,y,test_size=0.3,random_state=0) \n\n<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)<\/strong><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 3: \u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930 \u092c\u0928\u093e\u090f\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0915\u0947 \u092c\u093e\u0926, \u0939\u092e \u0935\u093e\u0938\u094d\u0924\u0935\u093f\u0915 \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u0926\u0930 \u0914\u0930 \u0917\u0932\u0924 \u0938\u0915\u093e\u0930\u093e\u0924\u094d\u092e\u0915 \u0926\u0930 \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0930\u0947\u0902\u0917\u0947 \u0914\u0930 \u092e\u0948\u091f\u092a\u094d\u0932\u094b\u091f\u0932\u093f\u092c \u0921\u0947\u091f\u093e \u0935\u093f\u091c\u093c\u0941\u0905\u0932\u093e\u0907\u091c\u093c\u0947\u0936\u0928 \u092a\u0948\u0915\u0947\u091c \u0915\u093e \u0909\u092a\u092f\u094b\u0917 \u0915\u0930\u0915\u0947 \u090f\u0915 \u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930 \u092c\u0928\u093e\u090f\u0902\u0917\u0947:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics\n<\/span>y_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)\n\n<span style=\"color: #008080;\">#create ROC curve\n<\/span>plt. <span style=\"color: #3366ff;\">plot<\/span> (fpr,tpr)\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">True Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">False Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-15772 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/rocpython1.png\" alt=\"\" width=\"399\" height=\"267\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\u0935\u0915\u094d\u0930 \u092a\u094d\u0932\u0949\u091f \u0915\u0947 \u090a\u092a\u0930\u0940 \u092c\u093e\u090f\u0901 \u0915\u094b\u0928\u0947 \u0915\u0947 \u091c\u093f\u0924\u0928\u093e \u0915\u0930\u0940\u092c \u092b\u093f\u091f \u0939\u094b\u0917\u093e, \u092e\u0949\u0921\u0932 \u0909\u0924\u0928\u093e \u0939\u0940 \u092c\u0947\u0939\u0924\u0930 \u0922\u0902\u0917 \u0938\u0947 \u0921\u0947\u091f\u093e \u0915\u094b \u0936\u094d\u0930\u0947\u0923\u093f\u092f\u094b\u0902 \u092e\u0947\u0902 \u0935\u0930\u094d\u0917\u0940\u0915\u0943\u0924 \u0915\u0930\u0928\u0947 \u092e\u0947\u0902 \u0938\u0915\u094d\u0937\u092e \u0939\u094b\u0917\u093e\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u091c\u0948\u0938\u093e \u0915\u093f \u0939\u092e \u090a\u092a\u0930 \u0926\u093f\u090f \u0917\u090f \u0917\u094d\u0930\u093e\u092b\u093c \u0938\u0947 \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902, \u092f\u0939 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0921\u0947\u091f\u093e \u0915\u094b \u0936\u094d\u0930\u0947\u0923\u093f\u092f\u094b\u0902 \u092e\u0947\u0902 \u0915\u094d\u0930\u092e\u092c\u0926\u094d\u0927 \u0915\u0930\u0928\u0947 \u0915\u093e \u092c\u0939\u0941\u0924 \u0916\u0930\u093e\u092c \u0915\u093e\u092e \u0915\u0930\u0924\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0907\u0938\u0947 \u092e\u093e\u092a\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f, \u0939\u092e \u090f\u092f\u0942\u0938\u0940 &#8211; \u0935\u0915\u094d\u0930 \u0915\u0947 \u0928\u0940\u091a\u0947 \u0915\u093e \u0915\u094d\u0937\u0947\u0924\u094d\u0930 &#8211; \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0930 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902 \u091c\u094b \u0939\u092e\u0947\u0902 \u092c\u0924\u093e\u0924\u093e \u0939\u0948 \u0915\u093f \u092a\u094d\u0932\u0949\u091f \u0915\u093e \u0915\u093f\u0924\u0928\u093e \u0939\u093f\u0938\u094d\u0938\u093e \u0935\u0915\u094d\u0930 \u0915\u0947 \u0928\u0940\u091a\u0947 \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\">AUC 1 \u0915\u0947 \u091c\u093f\u0924\u0928\u093e \u0915\u0930\u0940\u092c \u0939\u094b\u0917\u093e, \u092e\u0949\u0921\u0932 \u0909\u0924\u0928\u093e \u0939\u0940 \u092c\u0947\u0939\u0924\u0930 \u0939\u094b\u0917\u093e\u0964 0.5 \u0915\u0947 \u092c\u0930\u093e\u092c\u0930 \u090f\u092f\u0942\u0938\u0940 \u0935\u093e\u0932\u093e \u092e\u0949\u0921\u0932 \u092f\u093e\u0926\u0943\u091a\u094d\u091b\u093f\u0915 \u0935\u0930\u094d\u0917\u0940\u0915\u0930\u0923 \u0915\u0930\u0928\u0947 \u0935\u093e\u0932\u0947 \u092e\u0949\u0921\u0932 \u0938\u0947 \u092c\u0947\u0939\u0924\u0930 \u0928\u0939\u0940\u0902 \u0939\u0948\u0964<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u091a\u0930\u0923 4: \u090f\u092f\u0942\u0938\u0940 \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0930\u0947\u0902<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0939\u092e \u092e\u0949\u0921\u0932 \u0915\u0947 \u090f\u092f\u0942\u0938\u0940 \u0915\u0940 \u0917\u0923\u0928\u093e \u0915\u0930\u0928\u0947 \u0914\u0930 \u0907\u0938\u0947 \u0906\u0930\u0913\u0938\u0940 \u092a\u094d\u0932\u0949\u091f \u0915\u0947 \u0928\u093f\u091a\u0932\u0947 \u0926\u093e\u090f\u0902 \u0915\u094b\u0928\u0947 \u092e\u0947\u0902 \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;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define metrics\n<\/span>y_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: #ff0000;\">AUC=<\/span> \"+str(auc))\nplt. <span style=\"color: #3366ff;\">ylabel<\/span> (' <span style=\"color: #ff0000;\">True Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">xlabel<\/span> (' <span style=\"color: #ff0000;\">False Positive Rate<\/span> ')\nplt. <span style=\"color: #3366ff;\">legend<\/span> (loc=4)\nplt. <span style=\"color: #3366ff;\">show<\/span> ()<\/strong><\/span> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\" wp-image-15773 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/rocpython2.png\" alt=\"\" width=\"404\" height=\"275\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\u0907\u0938 \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0915\u093e AUC <strong>0.5602<\/strong> \u0928\u093f\u0915\u0932\u093e\u0964 \u091a\u0942\u0902\u0915\u093f \u092f\u0939 \u0906\u0902\u0915\u0921\u093c\u093e 0.5 \u092a\u0930 \u092c\u0902\u0926 \u0939\u0948, \u092f\u0939 \u092a\u0941\u0937\u094d\u091f\u093f \u0915\u0930\u0924\u093e \u0939\u0948 \u0915\u093f \u092e\u0949\u0921\u0932 \u0921\u0947\u091f\u093e \u0915\u094b \u0935\u0930\u094d\u0917\u0940\u0915\u0943\u0924 \u0915\u0930\u0928\u0947 \u0915\u093e \u0916\u0930\u093e\u092c \u0915\u093e\u092e \u0915\u0930 \u0930\u0939\u093e \u0939\u0948\u0964<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>\u0938\u0902\u092c\u0902\u0927\u093f\u0924:<\/strong> <a href=\"https:\/\/statorials.org\/hi\/\u090f\u0915\u093e\u0927\u093f\u0915-\u0935\u0915\u094d\u0930-\u092c\u0928\u093e\u090f\u0902-\u0930\u0949\u0915-\u092a\u093e\u092f\u0925\u0928\/\">\u092a\u093e\u092f\u0925\u0928 \u092e\u0947\u0902 \u090f\u0915\u093e\u0927\u093f\u0915 \u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930 \u0915\u0948\u0938\u0947 \u092a\u094d\u0932\u0949\u091f \u0915\u0930\u0947\u0902<\/a><\/span><\/p>\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 \u0938\u093e\u0902\u0916\u094d\u092f\u093f\u0915\u0940\u092f \u092a\u0926\u094d\u0927\u0924\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\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 \u092f\u0939 \u092e\u0942\u0932\u094d\u092f\u093e\u0902\u0915\u0928 \u0915\u0930\u0928\u0947 \u0915\u0947 \u0932\u093f\u090f \u0915\u093f \u0932\u0949\u091c\u093f\u0938\u094d\u091f\u093f\u0915 \u0930\u093f\u0917\u094d\u0930\u0947\u0936\u0928 \u092e\u0949\u0921\u0932 \u0921\u0947\u091f\u093e \u0938\u0947\u091f \u092e\u0947\u0902 \u0915\u093f\u0924\u0928\u0940 \u0905\u091a\u094d\u091b\u0940 \u0924\u0930\u0939 \u092b\u093f\u091f \u092c\u0948\u0920\u0924\u093e \u0939\u0948, \u0939\u092e \u0928\u093f\u092e\u094d\u0928\u0932\u093f\u0916\u093f\u0924 \u0926\u094b \u092e\u0948\u091f\u094d\u0930\u093f\u0915\u094d\u0938 \u0926\u0947\u0916 \u0938\u0915\u0924\u0947 \u0939\u0948\u0902: \u0938\u0902\u0935\u0947\u0926\u0928\u0936\u0940\u0932\u0924\u093e: \u0938\u0902\u092d\u093e\u0935\u0928\u093e \u0939\u0948 \u0915\u093f \u092e\u0949\u0921\u0932 \u0915\u093f\u0938\u0940 [&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-1584","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 \u0906\u0930\u0913\u0938\u0940 \u0935\u0915\u094d\u0930 \u0915\u0948\u0938\u0947 \u092c\u0928\u093e\u090f\u0902 (\u091a\u0930\u0923 \u0926\u0930 \u091a\u0930\u0923) - \u0938\u093e\u0902\u0916\u094d\u092f\u093f\u0915\u0940<\/title>\n<meta name=\"description\" content=\"\u092f\u0939 \u091f\u094d\u092f\u0942\u091f\u094b\u0930\u093f\u092f\u0932 \u091a\u0930\u0923-\u0926\u0930-\u091a\u0930\u0923 \u0909\u0926\u093e\u0939\u0930\u0923 \u0915\u0947 \u0938\u093e\u0925 \u092c\u0924\u093e\u0924\u093e 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