{"id":1198,"date":"2023-07-27T07:49:23","date_gmt":"2023-07-27T07:49:23","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d9%84%d8%a7%d8%b3%d9%88-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-27T07:49:23","modified_gmt":"2023-07-27T07:49:23","slug":"%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d9%84%d8%a7%d8%b3%d9%88-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/statorials.org\/ar\/%d8%a7%d9%86%d8%ad%d8%af%d8%a7%d8%b1-%d9%84%d8%a7%d8%b3%d9%88-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0627\u0646\u062d\u062f\u0627\u0631 \u0644\u0627\u0633\u0648 \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;\">\u064a\u0639\u062f <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0644\u0627\u0633\u0648\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0627\u0646\u062d\u062f\u0627\u0631 Lasso<\/a> \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 \u0648\u062c\u0648\u062f <a href=\"https:\/\/statorials.org\/ar\/\u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631-\u0645\u062a\u0639\u062f\u062f-\u0627\u0644\u062e\u0637\u064a\u0629\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0639\u0644\u0627\u0642\u0629 \u062e\u0637\u064a\u0629 \u0645\u062a\u0639\u062f\u062f\u0629<\/a> \u0641\u064a \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0628\u0627\u062e\u062a\u0635\u0627\u0631\u060c \u064a\u062d\u0627\u0648\u0644 \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u062a\u0642\u062f\u064a\u0631\u0627\u062a \u0644\u0644\u0645\u0639\u0627\u0645\u0644 \u062a\u0642\u0644\u0644 \u0645\u0646 \u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 (RSS):<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>RSS = \u03a3(\u0635 <sub>\u0637<\/sub> \u2013 \u0177 <sub>\u0637<\/sub> )2<\/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>\u03a3<\/strong> : \u0631\u0645\u0632 \u064a\u0648\u0646\u0627\u0646\u064a \u064a\u0639\u0646\u064a <em>\u0627\u0644\u0645\u0628\u0644\u063a<\/em><\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>y <sub>i<\/sub><\/strong> : \u0642\u064a\u0645\u0629 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0627\u0644\u0641\u0639\u0644\u064a\u0629 \u0644\u0644\u0645\u0644\u0627\u062d\u0638\u0629 <sup>\u0631\u0642\u0645 i<\/sup><\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0177 <sub>i<\/sub><\/strong> : \u0642\u064a\u0645\u0629 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0646\u0645\u0648\u0630\u062c \u0627\u0644\u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u062e\u0637\u064a \u0627\u0644\u0645\u062a\u0639\u062f\u062f<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0639\u0644\u0649 \u0627\u0644\u0639\u0643\u0633 \u0645\u0646 \u0630\u0644\u0643\u060c \u064a\u0633\u0639\u0649 \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0627\u0633\u0648 \u0625\u0644\u0649 \u062a\u0642\u0644\u064a\u0644 \u0645\u0627 \u064a\u0644\u064a:<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>RSS + \u03a3|\u03b2 <sub>\u064a<\/sub> |<\/strong><\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u062d\u064a\u062b \u062a\u0646\u062a\u0642\u0644 <em>j<\/em> \u0645\u0646 1 \u0625\u0644\u0649 \u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u062a\u0648\u0642\u0639 <em>p<\/em> \u0648 \u2265 \u2265 0.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u064f\u0639\u0631\u0641 \u0647\u0630\u0627 \u0627\u0644\u062d\u062f \u0627\u0644\u062b\u0627\u0646\u064a \u0641\u064a \u0627\u0644\u0645\u0639\u0627\u062f\u0644\u0629 <em>\u0628\u0639\u0642\u0648\u0628\u0629 \u0627\u0644\u0627\u0646\u0633\u062d\u0627\u0628<\/em> . \u0641\u064a \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0627\u0633\u0648\u060c \u0646\u062e\u062a\u0627\u0631 \u0642\u064a\u0645\u0629 \u03c0 \u0627\u0644\u062a\u064a \u062a\u0646\u062a\u062c \u0623\u0642\u0644 \u0627\u062e\u062a\u0628\u0627\u0631 \u0645\u0645\u0643\u0646 \u0644\u0640 MSE (\u0645\u062a\u0648\u0633\u0637 \u0645\u0631\u0628\u0639 \u0627\u0644\u062e\u0637\u0623).<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0642\u062f\u0645 \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 \u062d\u0648\u0644 \u0643\u064a\u0641\u064a\u0629 \u062a\u0646\u0641\u064a\u0630 \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0627\u0633\u0648 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646.<\/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\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0627\u0633\u0648 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">from<\/span> numpy <span style=\"color: #008000;\">import<\/span> arange\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LassoCV\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedKFold<\/strong><\/span><\/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\u0646\u0633\u062a\u062e\u062f\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a \u062a\u0633\u0645\u0649 <strong>mtcars<\/strong> \u060c \u0648\u0627\u0644\u062a\u064a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 \u0645\u0639\u0644\u0648\u0645\u0627\u062a \u0639\u0646 33 \u0633\u064a\u0627\u0631\u0629 \u0645\u062e\u062a\u0644\u0641\u0629. \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 <strong>hp<\/strong> \u0643\u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0648\u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u062a\u0646\u0628\u0624\u0627\u062a:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u064a\u0644\u0627 \u0641\u064a \u0627\u0644\u063a\u0627\u0644\u0648\u0646<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u0632\u0646<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0627\u0644\u0642\u0631\u0641<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">com.qsec<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u062a\u062d\u0645\u064a\u0644 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0647\u0630\u0647 \u0648\u0639\u0631\u0636\u0647\u0627:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define URL where data is located<\/span>\nurl = \"https:\/\/raw.githubusercontent.com\/- Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data<\/span>\ndata_full = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#select subset of data\n<\/span>data = data_full[[\"mpg\", \"wt\", \"drat\", \"qsec\", \"hp\"]]\n\n<span style=\"color: #008080;\">#view first six rows of data<\/span>\ndata[0:6]\n\n\tmpg wt drat qsec hp\n0 21.0 2.620 3.90 16.46 110\n1 21.0 2.875 3.90 17.02 110\n2 22.8 2.320 3.85 18.61 93\n3 21.4 3.215 3.08 19.44 110\n4 18.7 3,440 3.15 17.02 175\n5 18.1 3.460 2.76 20.22 105<\/strong><\/span><\/pre>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 3: \u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 Lasso<\/strong><\/span><\/h3>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0628\u0639\u062f \u0630\u0644\u0643\u060c \u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0648\u0638\u064a\u0641\u0629 <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.linear_model.RidgeCV.html\" target=\"_blank\" rel=\"noopener noreferrer\">LassoCV()<\/a> \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0640 sklearn \u0644\u0645\u0644\u0627\u0621\u0645\u0629 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 lasso \u0648\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.model_selection.RepeatedKFold.html\" target=\"_blank\" rel=\"noopener noreferrer\">RepeatedKFold()<\/a> \u0644\u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 \u0645\u0646 k-fold \u0644\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u0642\u064a\u0645\u0629 \u0623\u0644\u0641\u0627 \u0627\u0644\u0645\u062b\u0627\u0644\u064a\u0629 \u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627 \u0641\u064a \u0645\u062f\u0629 \u0627\u0644\u0639\u0642\u0648\u0628\u0629.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><em><strong>\u0645\u0644\u0627\u062d\u0638\u0629:<\/strong> \u064a\u064f\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0645\u0635\u0637\u0644\u062d &#8220;alpha&#8221; \u0628\u062f\u0644\u0627\u064b \u0645\u0646 &#8220;lambda&#8221; \u0641\u064a \u0644\u063a\u0629 Python.<\/em><\/span><\/p>\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\u062e\u062a\u0627\u0631 k = 10 \u0623\u0636\u0639\u0627\u0641 \u0648\u0646\u0643\u0631\u0631 \u0639\u0645\u0644\u064a\u0629 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 3 \u0645\u0631\u0627\u062a.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0644\u0627\u062d\u0638 \u0623\u064a\u0636\u064b\u0627 \u0623\u0646 LassoCV() \u064a\u062e\u062a\u0628\u0631 \u0641\u0642\u0637 \u0642\u064a\u0645 \u0623\u0644\u0641\u0627 0\u060c1\u060c 1\u060c \u064810 \u0627\u0641\u062a\u0631\u0627\u0636\u064a\u064b\u0627. \u0648\u0645\u0639 \u0630\u0644\u0643\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u0636\u0628\u0637 \u0646\u0637\u0627\u0642 \u0623\u0644\u0641\u0627 \u0627\u0644\u062e\u0627\u0635 \u0628\u0646\u0627 \u0645\u0646 0 \u0625\u0644\u0649 1 \u0628\u0632\u064a\u0627\u062f\u0627\u062a \u0642\u062f\u0631\u0647\u0627 0.01:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = data[[\"mpg\", \"wt\", \"drat\", \"qsec\"]]\ny = data[\"hp\"]\n\n<span style=\"color: #008080;\">#define cross-validation method to evaluate model\n<\/span>cv = RepeatedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n\n<span style=\"color: #008080;\">#define model\n<\/span>model = LassoCV(alphas= <span style=\"color: #3366ff;\">arange<\/span> (0, 1, 0.01), cv=cv, n_jobs=<\/strong><\/span> <span style=\"color: #008000;\"><strong>-1<\/strong><\/span> <span style=\"color: #000000;\"><strong>)\n\n<span style=\"color: #008080;\">#fit model\n<\/span>model. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n\n<span style=\"color: #008080;\">#display lambda that produced the lowest test MSE\n<\/span>print( <span style=\"color: #3366ff;\">model.alpha_<\/span> )\n\n0.99<\/strong><\/span><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u062a\u0628\u064a\u0646 \u0623\u0646 \u0642\u064a\u0645\u0629 \u0644\u0627\u0645\u062f\u0627 \u0627\u0644\u062a\u064a \u062a\u0642\u0644\u0644 \u0645\u0646 MSE \u0644\u0644\u0627\u062e\u062a\u0628\u0627\u0631 \u0647\u064a <strong>0.99<\/strong> .<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 4: \u0627\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0644\u0639\u0645\u0644 \u062a\u0646\u0628\u0624\u0627\u062a<\/strong><\/span><\/h3>\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 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0627\u0633\u0648 \u0627\u0644\u0646\u0647\u0627\u0626\u064a \u0644\u0644\u062a\u0646\u0628\u0624 \u0628\u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0627\u062a \u0627\u0644\u062c\u062f\u064a\u062f\u0629. \u0639\u0644\u0649 \u0633\u0628\u064a\u0644 \u0627\u0644\u0645\u062b\u0627\u0644\u060c \u064a\u0648\u0636\u062d \u0627\u0644\u0643\u0648\u062f \u0627\u0644\u062a\u0627\u0644\u064a \u0643\u064a\u0641\u064a\u0629 \u062a\u0639\u0631\u064a\u0641 \u0633\u064a\u0627\u0631\u0629 \u062c\u062f\u064a\u062f\u0629 \u0628\u0627\u0644\u0635\u0641\u0627\u062a \u0627\u0644\u062a\u0627\u0644\u064a\u0629:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0645\u064a\u0644\u0627 \u0641\u064a \u0627\u0644\u063a\u0627\u0644\u0648\u0646: 24<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0627\u0644\u0648\u0632\u0646: 2.5<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0627\u0644\u0633\u0639\u0631: 3.5<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0643\u064a\u0648 \u0633\u064a\u0643: 18.5<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0648\u0636\u062d \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u0629 \u0627\u0644\u0628\u0631\u0645\u062c\u064a\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0643\u064a\u0641\u064a\u0629 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0646\u0645\u0648\u0630\u062c \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0644\u0627\u0633\u0648 \u0627\u0644\u0645\u064f\u062c\u0647\u0632 \u0644\u0644\u062a\u0646\u0628\u0624 \u0628\u0642\u064a\u0645\u0629 <em>HP<\/em> \u0644\u0647\u0630\u0647 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0629 \u0627\u0644\u062c\u062f\u064a\u062f\u0629:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define new observation\n<span style=\"color: #000000;\">new = [24, 2.5, 3.5, 18.5]\n<\/span>\n#predict hp value using lasso regression model\n<span style=\"color: #000000;\">model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n<\/span>\n<span style=\"color: #000000;\">array([105.63442071])\n<\/span><\/span><\/strong><\/span><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u0628\u0646\u0627\u0621 \u0639\u0644\u0649 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u062f\u062e\u0644\u0629\u060c \u064a\u062a\u0648\u0642\u0639 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0623\u0646 \u062a\u0628\u0644\u063a <em>\u0642\u0648\u0629<\/em> \u0647\u0630\u0647 \u0627\u0644\u0633\u064a\u0627\u0631\u0629 <strong>105.63442071<\/strong> \u062d\u0635\u0627\u0646.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0645\u0643\u0646\u0643 \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\u0645\u062b\u0627\u0644 <a href=\"https:\/\/github.com\/- Statorials\/Python-Guides\/blob\/main\/lasso_regression.py\" target=\"_blank\" rel=\"noopener noreferrer\">\u0647\u0646\u0627<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u064a\u0639\u062f \u0627\u0646\u062d\u062f\u0627\u0631 Lasso \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 \u0648\u062c\u0648\u062f \u0639\u0644\u0627\u0642\u0629 \u062e\u0637\u064a\u0629 \u0645\u062a\u0639\u062f\u062f\u0629 \u0641\u064a \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a. \u0628\u0627\u062e\u062a\u0635\u0627\u0631\u060c \u064a\u062d\u0627\u0648\u0644 \u0627\u0646\u062d\u062f\u0627\u0631 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0635\u063a\u0631\u0649 \u0627\u0644\u0639\u062b\u0648\u0631 \u0639\u0644\u0649 \u062a\u0642\u062f\u064a\u0631\u0627\u062a \u0644\u0644\u0645\u0639\u0627\u0645\u0644 \u062a\u0642\u0644\u0644 \u0645\u0646 \u0645\u062c\u0645\u0648\u0639 \u0627\u0644\u0645\u0631\u0628\u0639\u0627\u062a \u0627\u0644\u0645\u062a\u0628\u0642\u064a\u0629 (RSS): RSS = \u03a3(\u0635 \u0637 \u2013 \u0177 \u0637 )2 \u0630\u0647\u0628: \u03a3 : \u0631\u0645\u0632 \u064a\u0648\u0646\u0627\u0646\u064a \u064a\u0639\u0646\u064a \u0627\u0644\u0645\u0628\u0644\u063a y i : \u0642\u064a\u0645\u0629 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0627\u0644\u0641\u0639\u0644\u064a\u0629 \u0644\u0644\u0645\u0644\u0627\u062d\u0638\u0629 \u0631\u0642\u0645 i [&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>\u0627\u0646\u062d\u062f\u0627\u0631 \u0644\u0627\u0633\u0648 \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646 (\u062e\u0637\u0648\u0629 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