{"id":3119,"date":"2023-07-19T03:05:15","date_gmt":"2023-07-19T03:05:15","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%aa%d9%82%d8%b1%d9%8a%d8%b1-%d8%aa%d8%b5%d9%86%d9%8a%d9%81-sklearn\/"},"modified":"2023-07-19T03:05:15","modified_gmt":"2023-07-19T03:05:15","slug":"%d8%aa%d9%82%d8%b1%d9%8a%d8%b1-%d8%aa%d8%b5%d9%86%d9%8a%d9%81-sklearn","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%aa%d9%82%d8%b1%d9%8a%d8%b1-%d8%aa%d8%b5%d9%86%d9%8a%d9%81-sklearn\/","title":{"rendered":"\u0643\u064a\u0641\u064a\u0629 \u062a\u0641\u0633\u064a\u0631 \u062a\u0642\u0631\u064a\u0631 \u0627\u0644\u062a\u0635\u0646\u064a\u0641 \u0641\u064a sklearn (\u0645\u0639 \u0645\u062b\u0627\u0644)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p style=\";text-align:right;direction:rtl\"><span style=\"color: #000000;\">\u0639\u0646\u062f\u0645\u0627 \u0646\u0633\u062a\u062e\u062f\u0645 <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0645\u0642\u0627\u0628\u0644-\u0627\u0644\u062a\u0635\u0646\u064a\u0641\/\" target=\"_blank\" rel=\"noopener\">\u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u0635\u0646\u064a\u0641<\/a> \u0641\u064a \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a\u060c \u0641\u0625\u0646\u0646\u0627 \u0646\u0633\u062a\u062e\u062f\u0645 \u062b\u0644\u0627\u062b\u0629 \u0645\u0642\u0627\u064a\u064a\u0633 \u0634\u0627\u0626\u0639\u0629 \u0644\u062a\u0642\u064a\u064a\u0645 \u062c\u0648\u062f\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>1. \u0627\u0644\u062f\u0642\u0629<\/strong> : \u0646\u0633\u0628\u0629 \u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629 \u0627\u0644\u0635\u062d\u064a\u062d\u0629 \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0625\u062c\u0645\u0627\u0644\u064a \u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>2. \u0627\u0644\u0627\u0633\u062a\u062f\u0639\u0627\u0621<\/strong> : \u0646\u0633\u0628\u0629 \u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629 \u0627\u0644\u0635\u062d\u064a\u062d\u0629 \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0625\u062c\u0645\u0627\u0644\u064a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0627\u062a \u0627\u0644\u0641\u0639\u0644\u064a\u0629.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>3. \u062f\u0631\u062c\u0629 F1<\/strong> : \u0627\u0644\u0645\u062a\u0648\u0633\u0637 \u0627\u0644\u062a\u0648\u0627\u0641\u0642\u064a \u0627\u0644\u0645\u0631\u062c\u062d \u0644\u0644\u062f\u0642\u0629 \u0648\u0627\u0644\u0627\u0633\u062a\u0630\u0643\u0627\u0631. \u0643\u0644\u0645\u0627 \u0643\u0627\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0623\u0642\u0631\u0628 \u0625\u0644\u0649 1\u060c \u0643\u0627\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0623\u0641\u0636\u0644.<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062f\u0631\u062c\u0629 F1: 2* (\u0627\u0644\u062f\u0642\u0629 * \u0627\u0644\u0627\u0633\u062a\u062f\u0639\u0627\u0621) \/ (\u0627\u0644\u062f\u0642\u0629 + \u0627\u0644\u0627\u0633\u062a\u062f\u0639\u0627\u0621)<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><span style=\"color: #000000;\">\u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0647\u0630\u0647 \u0627\u0644\u0645\u0642\u0627\u064a\u064a\u0633 \u0627\u0644\u062b\u0644\u0627\u062b\u0629\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0641\u0647\u0645 \u0645\u062f\u0649 \u0642\u062f\u0631\u0629 \u0646\u0645\u0648\u0630\u062c \u062a\u0635\u0646\u064a\u0641 \u0645\u0639\u064a\u0646 \u0639\u0644\u0649 \u0627\u0644\u062a\u0646\u0628\u0624 \u0628\u0627\u0644\u0646\u062a\u0627\u0626\u062c <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\">\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0633\u062a\u062c\u0627\u0628\u0629<\/a> \u0645\u0639\u064a\u0646\u0629.<\/span><\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u062d\u0633\u0646 \u0627\u0644\u062d\u0638\u060c \u0639\u0646\u062f \u062a\u0631\u0643\u064a\u0628 \u0646\u0645\u0648\u0630\u062c \u062a\u0635\u0646\u064a\u0641 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 <strong>Classification_report()<\/strong> \u0645\u0646 \u0645\u0643\u062a\u0628\u0629 <strong>sklearn<\/strong> \u0644\u0625\u0646\u0634\u0627\u0621 \u0647\u0630\u0647 \u0627\u0644\u0645\u0642\u0627\u064a\u064a\u0633 \u0627\u0644\u062b\u0644\u0627\u062b\u0629.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u0645\u062b\u0627\u0644 \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0647\u0630\u0647 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0639\u0645\u0644\u064a\u064b\u0627.<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u062b\u0627\u0644: \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u062a\u0642\u0631\u064a\u0631 \u0627\u0644\u062a\u0635\u0646\u064a\u0641 \u0641\u064a sklearn<\/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\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 \u0627\u0644\u0630\u064a \u064a\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0646\u0642\u0627\u0637 \u0648\u064a\u0633\u0627\u0639\u062f \u0639\u0644\u0649 \u0627\u0644\u062a\u0646\u0628\u0624 \u0628\u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0633\u064a\u062a\u0645 \u0636\u0645 1000 \u0644\u0627\u0639\u0628 \u0643\u0631\u0629 \u0633\u0644\u0629 \u062c\u0627\u0645\u0639\u064a \u0645\u062e\u062a\u0644\u0641 \u0625\u0644\u0649 \u0627\u0644\u062f\u0648\u0631\u064a \u0627\u0644\u0627\u0645\u064a\u0631\u0643\u064a \u0644\u0644\u0645\u062d\u062a\u0631\u0641\u064a\u0646 \u0623\u0645 \u0644\u0627.<\/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\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: #3366ff;\">metrics<\/span> <span style=\"color: #008000;\">import<\/span> classification_report<\/strong>\n<\/pre>\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\u0625\u0646\u0634\u0627\u0621 \u0625\u0637\u0627\u0631 \u0628\u064a\u0627\u0646\u0627\u062a \u064a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u0639\u0644\u0648\u0645\u0627\u062a 1000 \u0644\u0627\u0639\u0628 \u0643\u0631\u0629 \u0633\u0644\u0629:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#make this example reproducible\n<\/span>n.p. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">seeds<\/span> (1)\n\n<span style=\"color: #008080;\">#createDataFrame\n<\/span>df = pd. <span style=\"color: #3366ff;\">DataFrame<\/span> ({' <span style=\"color: #ff0000;\">points<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (30, size=1000),\n                   ' <span style=\"color: #ff0000;\">assists<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (12, size=1000),\n                   ' <span style=\"color: #ff0000;\">drafted<\/span> ': np. <span style=\"color: #3366ff;\">random<\/span> . <span style=\"color: #3366ff;\">randint<\/span> (2, size=1000)})\n\n<span style=\"color: #008080;\">#view DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n\tpoints assists drafted\n0 5 1 1\n1 11 8 0\n2 12 4 1\n3 8 7 0\n4 9 0 0\n<\/strong><\/span><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u0644\u062d\u0648\u0638\u0629<\/strong> : \u062a\u0634\u064a\u0631 \u0627\u0644\u0642\u064a\u0645\u0629 <strong>0<\/strong> \u0625\u0644\u0649 \u0623\u0646 \u0627\u0644\u0644\u0627\u0639\u0628 \u0644\u0645 \u062a\u062a\u0645 \u0635\u064a\u0627\u063a\u062a\u0647 \u0628\u064a\u0646\u0645\u0627 \u062a\u0634\u064a\u0631 \u0627\u0644\u0642\u064a\u0645\u0629 <strong>1<\/strong> \u0625\u0644\u0649 \u0623\u0646 \u0627\u0644\u0644\u0627\u0639\u0628 \u0642\u062f \u062a\u0645\u062a \u0635\u064a\u0627\u063a\u062a\u0647.<\/span><\/p>\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 \u0628\u064a\u0627\u0646\u0627\u062a\u0646\u0627 \u0625\u0644\u0649 \u0645\u062c\u0645\u0648\u0639\u0629 \u062a\u062f\u0631\u064a\u0628 \u0648\u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u062e\u062a\u0628\u0627\u0631 \u0648\u062a\u0646\u0627\u0633\u0628 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a:<\/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 = df[[' <span style=\"color: #ff0000;\">points<\/span> ', ' <span style=\"color: #ff0000;\">assists<\/span> ']]\ny = df[' <span style=\"color: #ff0000;\">drafted<\/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)  \n\n<\/strong><strong><span style=\"color: #008080;\">#instantiate the model\n<\/span>logistic_regression = LogisticRegression()\n\n<span style=\"color: #008080;\">#fit the model using the training data\n<\/span>logistic_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 = logistic_regression. <span style=\"color: #3366ff;\">predict<\/span> (X_test)<\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u0623\u062e\u064a\u0631\u064b\u0627\u060c \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0648\u0638\u064a\u0641\u0629 <strong>Classification_report()<\/strong> \u0644\u0637\u0628\u0627\u0639\u0629 \u0645\u0642\u0627\u064a\u064a\u0633 \u0627\u0644\u062a\u0635\u0646\u064a\u0641 \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0646\u0645\u0648\u0630\u062c\u0646\u0627:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#print classification report for model\n<span style=\"color: #000000;\"><span style=\"color: #008000;\">print<\/span> (classification_report(y_test, y_pred))\n\n              precision recall f1-score support\n\n           0 0.51 0.58 0.54 160\n           1 0.43 0.36 0.40 140\n\n    accuracy 0.48 300\n   macro avg 0.47 0.47 0.47 300\nweighted avg 0.47 0.48 0.47 300\n<\/span><\/span><\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u0625\u0644\u064a\u0643 \u0643\u064a\u0641\u064a\u0629 \u062a\u0641\u0633\u064a\u0631 \u0627\u0644\u0646\u062a\u064a\u062c\u0629:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u062a\u0648\u0636\u064a\u062d<\/strong> : \u0645\u0646 \u0628\u064a\u0646 \u062c\u0645\u064a\u0639 \u0627\u0644\u0644\u0627\u0639\u0628\u064a\u0646 \u0627\u0644\u0630\u064a\u0646 \u062a\u0648\u0642\u0639 \u0646\u0645\u0648\u0630\u062c\u0647\u0645 \u0623\u0646 \u064a\u062a\u0645 \u062a\u062c\u0646\u064a\u062f\u0647\u0645\u060c <strong>43%<\/strong> \u0641\u0642\u0637 \u062a\u0645 \u062a\u062c\u0646\u064a\u062f\u0647\u0645 \u0628\u0627\u0644\u0641\u0639\u0644.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u062a\u0630\u0643\u064a\u0631<\/strong> : \u0645\u0646 \u0628\u064a\u0646 \u062c\u0645\u064a\u0639 \u0627\u0644\u0644\u0627\u0639\u0628\u064a\u0646 \u0627\u0644\u0630\u064a\u0646 \u062a\u0645\u062a \u0635\u064a\u0627\u063a\u062a\u0647\u0645 \u0628\u0627\u0644\u0641\u0639\u0644\u060c \u062a\u0648\u0642\u0639 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0647\u0630\u0647 \u0627\u0644\u0646\u062a\u064a\u062c\u0629 \u0628\u0634\u0643\u0644 \u0635\u062d\u064a\u062d \u0641\u0642\u0637 \u0644\u0640 <strong>36%<\/strong> \u0645\u0646\u0647\u0645.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0646\u0642\u0627\u0637 F1<\/strong> : \u064a\u062a\u0645 \u062d\u0633\u0627\u0628 \u0647\u0630\u0647 \u0627\u0644\u0642\u064a\u0645\u0629 \u0639\u0644\u0649 \u0627\u0644\u0646\u062d\u0648 \u0627\u0644\u062a\u0627\u0644\u064a:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062f\u0631\u062c\u0629 F1: 2* (\u0627\u0644\u062f\u0642\u0629 * \u0627\u0644\u0627\u0633\u062a\u062f\u0639\u0627\u0621) \/ (\u0627\u0644\u062f\u0642\u0629 + \u0627\u0644\u0627\u0633\u062a\u062f\u0639\u0627\u0621)<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0646\u062a\u064a\u062c\u0629 F1: 2*(.43*.36)\/(.43+.36)<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0635\u0646\u064a\u0641 F1: <strong>0.40<\/strong> .<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0646\u0638\u0631\u064b\u0627 \u0644\u0623\u0646 \u0647\u0630\u0647 \u0627\u0644\u0642\u064a\u0645\u0629 \u0644\u064a\u0633\u062a \u0642\u0631\u064a\u0628\u0629 \u062c\u062f\u064b\u0627 \u0645\u0646 1\u060c \u0641\u0647\u0630\u0627 \u064a\u062e\u0628\u0631\u0646\u0627 \u0623\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0644\u0627 \u064a\u062a\u0646\u0628\u0623 \u0628\u0634\u0643\u0644 \u062c\u064a\u062f \u0628\u0645\u0627 \u0625\u0630\u0627 \u0643\u0627\u0646 \u0633\u064a\u062a\u0645 \u062a\u062c\u0646\u064a\u062f \u0627\u0644\u0644\u0627\u0639\u0628\u064a\u0646 \u0623\u0645 \u0644\u0627.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062f\u0639\u0645<\/strong> : \u062a\u062e\u0628\u0631\u0646\u0627 \u0647\u0630\u0647 \u0627\u0644\u0642\u064a\u0645 \u0628\u0628\u0633\u0627\u0637\u0629 \u0628\u0639\u062f\u062f \u0627\u0644\u0644\u0627\u0639\u0628\u064a\u0646 \u0627\u0644\u0630\u064a\u0646 \u064a\u0646\u062a\u0645\u0648\u0646 \u0625\u0644\u0649 \u0643\u0644 \u0641\u0626\u0629 \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631. \u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646\u0647 \u0645\u0646 \u0628\u064a\u0646 \u0627\u0644\u0644\u0627\u0639\u0628\u064a\u0646 \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u0627\u062e\u062a\u0628\u0627\u0631\u060c \u0647\u0646\u0627\u0643 <strong>160 \u0644\u0627\u0639\u0628\u064b\u0627<\/strong> \u0644\u0645 \u062a\u062a\u0645 \u0635\u064a\u0627\u063a\u062a\u0647\u0645 \u0648 <strong>140<\/strong> \u0643\u0627\u0646\u0648\u0627 \u0643\u0630\u0644\u0643.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u0644\u0627\u062d\u0638\u0629<\/strong> : \u064a\u0645\u0643\u0646\u0643 \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0627\u0644\u0648\u062b\u0627\u0626\u0642 \u0627\u0644\u0643\u0627\u0645\u0644\u0629 \u0644\u0648\u0638\u064a\u0641\u0629 <strong>Classification_report()<\/strong> <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.classification_report.html\" target=\"_blank\" rel=\"noopener\">\u0647\u0646\u0627<\/a> .<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0645\u0635\u0627\u062f\u0631 \u0625\u0636\u0627\u0641\u064a\u0629<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062a\u0648\u0641\u0631 \u0627\u0644\u0628\u0631\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0625\u0636\u0627\u0641\u064a\u0629 \u062d\u0648\u0644 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u0635\u0646\u064a\u0641 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0627\u0644\u0644\u0648\u062c\u0633\u062a\u064a-\u0628\u064a\u062b\u0648\u0646\/\" target=\"_blank\" rel=\"noopener\">\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<\/a><br \/> <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0631\u062a\u0628\u0627\u0643-\u0645\u0635\u0641\u0648\u0641\u0629-\u0628\u0627\u064a\u062b\u0648\u0646\/\" target=\"_blank\" rel=\"noopener\">\u0643\u064a\u0641\u064a\u0629 \u0625\u0646\u0634\u0627\u0621 \u0645\u0635\u0641\u0648\u0641\u0629 \u0627\u0644\u0627\u0631\u062a\u0628\u0627\u0643 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><br \/> <a href=\"https:\/\/statorials.org\/ar\/\u062f\u0642\u0629-\u0645\u062a\u0648\u0627\u0632\u0646\u0629-\u0628\u064a\u062b\u0648\u0646-sklearn\/\">\u0643\u064a\u0641\u064a\u0629 \u062d\u0633\u0627\u0628 \u0627\u0644\u062f\u0642\u0629 \u0627\u0644\u0645\u062a\u0648\u0627\u0632\u0646\u0629 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0639\u0646\u062f\u0645\u0627 \u0646\u0633\u062a\u062e\u062f\u0645 \u0646\u0645\u0627\u0630\u062c \u0627\u0644\u062a\u0635\u0646\u064a\u0641 \u0641\u064a \u0627\u0644\u062a\u0639\u0644\u0645 \u0627\u0644\u0622\u0644\u064a\u060c \u0641\u0625\u0646\u0646\u0627 \u0646\u0633\u062a\u062e\u062f\u0645 \u062b\u0644\u0627\u062b\u0629 \u0645\u0642\u0627\u064a\u064a\u0633 \u0634\u0627\u0626\u0639\u0629 \u0644\u062a\u0642\u064a\u064a\u0645 \u062c\u0648\u062f\u0629 \u0627\u0644\u0646\u0645\u0648\u0630\u062c: 1. \u0627\u0644\u062f\u0642\u0629 : \u0646\u0633\u0628\u0629 \u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629 \u0627\u0644\u0635\u062d\u064a\u062d\u0629 \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0625\u062c\u0645\u0627\u0644\u064a \u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629. 2. \u0627\u0644\u0627\u0633\u062a\u062f\u0639\u0627\u0621 : \u0646\u0633\u0628\u0629 \u0627\u0644\u062a\u0648\u0642\u0639\u0627\u062a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0629 \u0627\u0644\u0635\u062d\u064a\u062d\u0629 \u0645\u0642\u0627\u0631\u0646\u0629 \u0628\u0625\u062c\u0645\u0627\u0644\u064a \u0627\u0644\u0625\u064a\u062c\u0627\u0628\u064a\u0627\u062a \u0627\u0644\u0641\u0639\u0644\u064a\u0629. 3. \u062f\u0631\u062c\u0629 F1 : \u0627\u0644\u0645\u062a\u0648\u0633\u0637 \u0627\u0644\u062a\u0648\u0627\u0641\u0642\u064a \u0627\u0644\u0645\u0631\u062c\u062d \u0644\u0644\u062f\u0642\u0629 \u0648\u0627\u0644\u0627\u0633\u062a\u0630\u0643\u0627\u0631. \u0643\u0644\u0645\u0627 \u0643\u0627\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0623\u0642\u0631\u0628 \u0625\u0644\u0649 1\u060c \u0643\u0627\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0623\u0641\u0636\u0644. [&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 \u062a\u0641\u0633\u064a\u0631 \u062a\u0642\u0631\u064a\u0631 \u0627\u0644\u062a\u0635\u0646\u064a\u0641 \u0641\u064a sklearn (\u0645\u0639 \u0645\u062b\u0627\u0644) - \u0639\u0644\u0645 \u0627\u0644\u0625\u062d\u0635\u0627\u0621<\/title>\n<meta name=\"description\" content=\"\u064a\u0634\u0631\u062d \u0647\u0630\u0627 \u0627\u0644\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 Classification_report() \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\u060c \u0645\u0639 \u0645\u062b\u0627\u0644.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/ar\/\u062a\u0642\u0631\u064a\u0631-\u062a\u0635\u0646\u064a\u0641-sklearn\/\" \/>\n<meta property=\"og:locale\" content=\"az_AZ\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u0643\u064a\u0641\u064a\u0629 \u062a\u0641\u0633\u064a\u0631 \u062a\u0642\u0631\u064a\u0631 \u0627\u0644\u062a\u0635\u0646\u064a\u0641 \u0641\u064a sklearn (\u0645\u0639 \u0645\u062b\u0627\u0644) - 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