{"id":1163,"date":"2023-07-27T10:43:39","date_gmt":"2023-07-27T10:43:39","guid":{"rendered":"https:\/\/statorials.org\/ar\/%d8%a7%d9%84%d8%aa%d8%ad%d9%84%d9%8a%d9%84-%d8%a7%d9%84%d8%aa%d9%85%d9%8a%d9%8a%d8%b2%d9%8a-%d8%a7%d9%84%d8%ae%d8%b7%d9%8a-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/"},"modified":"2023-07-27T10:43:39","modified_gmt":"2023-07-27T10:43:39","slug":"%d8%a7%d9%84%d8%aa%d8%ad%d9%84%d9%8a%d9%84-%d8%a7%d9%84%d8%aa%d9%85%d9%8a%d9%8a%d8%b2%d9%8a-%d8%a7%d9%84%d8%ae%d8%b7%d9%8a-%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%84%d8%aa%d8%ad%d9%84%d9%8a%d9%84-%d8%a7%d9%84%d8%aa%d9%85%d9%8a%d9%8a%d8%b2%d9%8a-%d8%a7%d9%84%d8%ae%d8%b7%d9%8a-%d9%81%d9%8a-%d8%a8%d9%8a%d8%ab%d9%88%d9%86\/","title":{"rendered":"\u0627\u0644\u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a \u0627\u0644\u062e\u0637\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\u062a\u062d\u0644\u064a\u0644-\u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a-\u0627\u0644\u062e\u0637\u064a\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u0627\u0644\u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a \u0627\u0644\u062e\u0637\u064a<\/a> \u0647\u0648 \u0637\u0631\u064a\u0642\u0629 \u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627 \u0639\u0646\u062f\u0645\u0627 \u064a\u0643\u0648\u0646 \u0644\u062f\u064a\u0643 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0648\u062a\u0631\u063a\u0628 \u0641\u064a \u062a\u0635\u0646\u064a\u0641 <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> \u0625\u0644\u0649 \u0641\u0626\u062a\u064a\u0646 \u0623\u0648 \u0623\u0643\u062b\u0631.<\/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 \u0644\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a \u0627\u0644\u062e\u0637\u064a \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: \u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u0645\u0643\u062a\u0628\u0627\u062a \u0627\u0644\u0636\u0631\u0648\u0631\u064a\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\u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0648\u0627\u0644\u0645\u0643\u062a\u0628\u0627\u062a \u0627\u0644\u0644\u0627\u0632\u0645\u0629 \u0644\u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><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;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedStratifiedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> cross_val_score\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">discriminant_analysis<\/span> <span style=\"color: #008000;\">import<\/span> LinearDiscriminantAnalysis \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> datasets\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<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<\/b><\/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\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0645\u062c\u0645\u0648\u0639\u0629 \u0628\u064a\u0627\u0646\u0627\u062a <strong>iris<\/strong> \u0645\u0646 \u0645\u0643\u062a\u0628\u0629 sklearn. \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 \u062a\u062d\u0645\u064a\u0644 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0647\u0630\u0647 \u0648\u062a\u062d\u0648\u064a\u0644\u0647\u0627 \u0625\u0644\u0649 Pandas DataFrame \u0644\u0633\u0647\u0648\u0644\u0629 \u0627\u0644\u0627\u0633\u062a\u062e\u062f\u0627\u0645:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>iris<\/em> dataset<\/span>\niris = datasets. <span style=\"color: #3366ff;\">load_iris<\/span> ()\n\n<span style=\"color: #008080;\">#convert dataset to pandas DataFrame\n<\/span>df = pd.DataFrame(data = np.c_[iris[' <span style=\"color: #008000;\">data<\/span> '], iris[' <span style=\"color: #008000;\">target<\/span> ']],\n                 columns = iris[' <span style=\"color: #008000;\">feature_names<\/span> '] + [' <span style=\"color: #008000;\">target<\/span> '])\ndf[' <span style=\"color: #008000;\">species<\/span> '] = pd. <span style=\"color: #3366ff;\">Categorical<\/span> . <span style=\"color: #3366ff;\">from_codes<\/span> (iris.target, iris.target_names)\ndf.columns = [' <span style=\"color: #008000;\">s_length<\/span> ', ' <span style=\"color: #008000;\">s_width<\/span> ', ' <span style=\"color: #008000;\">p_length<\/span> ', ' <span style=\"color: #008000;\">p_width<\/span> ', ' <span style=\"color: #008000;\">target<\/span> ', ' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#view first six rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n   s_length s_width p_length p_width target species\n0 5.1 3.5 1.4 0.2 0.0 setosa\n1 4.9 3.0 1.4 0.2 0.0 setosa\n2 4.7 3.2 1.3 0.2 0.0 setosa\n3 4.6 3.1 1.5 0.2 0.0 setosa\n4 5.0 3.6 1.4 0.2 0.0 setosa\n\n<span style=\"color: #3366ff;\"><span style=\"color: #008080;\">#find how many total observations are in dataset<\/span>\n<span style=\"color: #000000;\">len( <span style=\"color: #3366ff;\">df.index<\/span> )\n\n150<\/span><\/span><\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u062a\u062d\u062a\u0648\u064a \u0639\u0644\u0649 150 \u0645\u0644\u0627\u062d\u0638\u0629 \u0641\u064a \u0627\u0644\u0645\u062c\u0645\u0648\u0639.<\/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\u0642\u0648\u0645 \u0628\u0628\u0646\u0627\u0621 \u0646\u0645\u0648\u0630\u062c \u062a\u062d\u0644\u064a\u0644 \u062a\u0645\u064a\u064a\u0632\u064a \u062e\u0637\u064a \u0644\u062a\u0635\u0646\u064a\u0641 \u0627\u0644\u0623\u0646\u0648\u0627\u0639 \u0627\u0644\u062a\u064a \u062a\u0646\u062a\u0645\u064a \u0625\u0644\u064a\u0647\u0627 \u0632\u0647\u0631\u0629 \u0645\u0639\u064a\u0646\u0629.<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0633\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0641\u064a \u0627\u0644\u0646\u0645\u0648\u0630\u062c:<\/span><\/p>\n<ul style=\";text-align:right;direction:rtl\">\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0637\u0648\u0644 \u0633\u064a\u0628\u0627\u0644<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0639\u0631\u0636 \u0633\u064a\u0628\u0627\u0644<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0637\u0648\u0644 \u0627\u0644\u0628\u062a\u0644\u0629<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0639\u0631\u0636 \u0627\u0644\u0628\u062a\u0644\u0629<\/span><\/li>\n<\/ul>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u0633\u0646\u0633\u062a\u062e\u062f\u0645\u0647\u0627 \u0644\u0644\u062a\u0646\u0628\u0624 \u0628\u0645\u062a\u063a\u064a\u0631 \u0627\u0633\u062a\u062c\u0627\u0628\u0629 <em>\u0627\u0644\u0623\u0646\u0648\u0627\u0639<\/em> \u060c \u0627\u0644\u0630\u064a \u064a\u062f\u0639\u0645 \u0627\u0644\u0641\u0626\u0627\u062a \u0627\u0644\u062b\u0644\u0627\u062b\u0629 \u0627\u0644\u0645\u062d\u062a\u0645\u0644\u0629 \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;\">\u0633\u064a\u062a\u0648\u0633\u0627<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0627\u0644\u0645\u0628\u0631\u0642\u0634\u0629<\/span><\/li>\n<li style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0641\u0631\u062c\u064a\u0646\u064a\u0627<\/span><\/li>\n<\/ul>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 3: \u0636\u0628\u0637 \u0646\u0645\u0648\u0630\u062c LDA<\/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\u0644\u0627\u0626\u0645 \u0646\u0645\u0648\u0630\u062c LDA \u0645\u0639 \u0628\u064a\u0627\u0646\u0627\u062a\u0646\u0627 \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0648\u0638\u064a\u0641\u0629 <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\" target=\"_blank\" rel=\"noopener noreferrer\">LinearDiscriminantAnalsys<\/a> \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0640 sklearn:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = df[[' <span style=\"color: #008000;\">s_length<\/span> ',' <span style=\"color: #008000;\">s_width<\/span> ',' <span style=\"color: #008000;\">p_length<\/span> ',' <span style=\"color: #008000;\">p_width<\/span> ']]\ny = df[' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#Fit the LDA model\n<\/span>model = LinearDiscriminantAnalysis()\nmodel. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n<\/strong><\/pre>\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;\">\u0628\u0645\u062c\u0631\u062f \u062a\u0631\u0643\u064a\u0628 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0628\u064a\u0627\u0646\u0627\u062a\u0646\u0627\u060c \u064a\u0645\u0643\u0646\u0646\u0627 \u062a\u0642\u064a\u064a\u0645 \u0623\u062f\u0627\u0621 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0628\u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u062a\u062d\u0642\u0642 \u0627\u0644\u0645\u062a\u0628\u0627\u062f\u0644 \u0627\u0644\u0637\u0628\u0642\u064a \u0627\u0644\u0645\u062a\u0643\u0631\u0631.<\/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\u0648\u0641 \u0646\u0633\u062a\u062e\u062f\u0645 10 \u0637\u064a\u0627\u062a \u06483 \u062a\u0643\u0631\u0627\u0631\u0627\u062a:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#Define method to evaluate model\n<span style=\"color: #000000;\">cv = RepeatedStratifiedKFold(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<\/span>\n#evaluate model\n<span style=\"color: #000000;\">scores = cross_val_score(model, X, y, scoring=' <span style=\"color: #008000;\">accuracy<\/span> ', cv=cv, n_jobs=-1)\nprint( <span style=\"color: #3366ff;\">np.mean<\/span> (scores))<\/span>  \n\n<span style=\"color: #000000;\">0.9777777777777779<\/span><\/span><\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u0646 \u0646\u0631\u0649 \u0623\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u062d\u0642\u0642 \u0645\u062a\u0648\u0633\u0637 \u062f\u0642\u0629 <strong>97.78%<\/strong> .<\/span><\/p>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u064a\u0645\u0643\u0646\u0646\u0627 \u0623\u064a\u0636\u064b\u0627 \u0627\u0633\u062a\u062e\u062f\u0627\u0645 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u0644\u0644\u062a\u0646\u0628\u0624 \u0628\u0627\u0644\u0641\u0626\u0629 \u0627\u0644\u062a\u064a \u062a\u0646\u062a\u0645\u064a \u0625\u0644\u064a\u0647\u0627 \u0627\u0644\u0632\u0647\u0631\u0629 \u0627\u0644\u062c\u062f\u064a\u062f\u0629\u060c \u0628\u0646\u0627\u0621\u064b \u0639\u0644\u0649 \u0627\u0644\u0642\u064a\u0645 \u0627\u0644\u0645\u062f\u062e\u0644\u0629:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new observation\n<\/span>new = [5, 3, 1, .4]\n\n<span style=\"color: #008080;\">#predict which class the new observation belongs to\n<\/span>model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n\narray(['setosa'], dtype='&lt;U10')\n<\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\">\u0648\u0646\u0631\u0649 \u0623\u0646 \u0627\u0644\u0646\u0645\u0648\u0630\u062c \u064a\u062a\u0646\u0628\u0623 \u0628\u0623\u0646 \u0647\u0630\u0647 \u0627\u0644\u0645\u0644\u0627\u062d\u0638\u0629 \u0627\u0644\u062c\u062f\u064a\u062f\u0629 \u062a\u0646\u062a\u0645\u064a \u0625\u0644\u0649 \u0646\u0648\u0639 \u064a\u0633\u0645\u0649 <em>\u0633\u064a\u062a\u0648\u0633\u0627<\/em> .<\/span><\/p>\n<h3 style=\";text-align:right;direction:rtl\"> <span style=\"color: #000000;\"><strong>\u0627\u0644\u062e\u0637\u0648\u0629 5: \u062a\u0635\u0648\u0631 \u0627\u0644\u0646\u062a\u0627\u0626\u062c<\/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 \u0625\u0646\u0634\u0627\u0621 \u0645\u062e\u0637\u0637 LDA \u0644\u062a\u0635\u0648\u0631 \u0627\u0644\u0645\u0645\u064a\u0632\u0627\u062a \u0627\u0644\u062e\u0637\u064a\u0629 \u0644\u0644\u0646\u0645\u0648\u0630\u062c \u0648\u062a\u0635\u0648\u0631 \u0645\u062f\u0649 \u0646\u062c\u0627\u062d\u0647 \u0641\u064a \u0627\u0644\u0641\u0635\u0644 \u0628\u064a\u0646 \u0627\u0644\u0623\u0646\u0648\u0627\u0639 \u0627\u0644\u062b\u0644\u0627\u062b\u0629 \u0627\u0644\u0645\u062e\u062a\u0644\u0641\u0629 \u0641\u064a \u0645\u062c\u0645\u0648\u0639\u0629 \u0627\u0644\u0628\u064a\u0627\u0646\u0627\u062a \u0627\u0644\u062e\u0627\u0635\u0629 \u0628\u0646\u0627:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define data to plot\n<\/span>X = iris.data\ny = iris.target\nmodel = LinearDiscriminantAnalysis()\ndata_plot = model. <span style=\"color: #3366ff;\">fit<\/span> (x,y). <span style=\"color: #3366ff;\">transform<\/span> (X)\ntarget_names = iris. <span style=\"color: #3366ff;\">target_names<\/span>\n\n<span style=\"color: #008080;\">#create LDA plot\n<\/span>plt. <span style=\"color: #3366ff;\">figure<\/span> ()\ncolors = [' <span style=\"color: #008000;\">red<\/span> ', ' <span style=\"color: #008000;\">green<\/span> ', ' <span style=\"color: #008000;\">blue<\/span> ']\nlw = 2\n<span style=\"color: #008000;\">for<\/span> color, i, target_name <span style=\"color: #008000;\">in<\/span> zip(colors, [0, 1, 2], target_names):\n    plt. <span style=\"color: #3366ff;\">scatter<\/span> (data_plot[y == i, 0], data_plot[y == i, 1], alpha=.8, color=color,\n                label=target_name)\n\n<span style=\"color: #008080;\">#add legend to plot\n<\/span>plt. <span style=\"color: #3366ff;\">legend<\/span> (loc=' <span style=\"color: #008000;\">best<\/span> ', shadow= <span style=\"color: #008000;\">False<\/span> , scatterpoints=1)\n\n<span style=\"color: #008080;\">#display LDA plot\n<\/span>plt. <span style=\"color: #3366ff;\">show<\/span> ()\n<\/strong><\/pre>\n<p style=\";text-align:right;direction:rtl\"><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11651 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ldapython1.png\" alt=\"\u0627\u0644\u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a \u0627\u0644\u062e\u0637\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646\" width=\"416\" height=\"281\" srcset=\"\" sizes=\"\"><\/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\u0628\u0631\u0646\u0627\u0645\u062c \u0627\u0644\u062a\u0639\u0644\u064a\u0645\u064a <a href=\"https:\/\/github.com\/- Statorials\/Python-Guides\/blob\/main\/linear_discriminant_analysis\" target=\"_blank\" rel=\"noopener noreferrer\">\u0647\u0646\u0627<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u0627\u0644\u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a \u0627\u0644\u062e\u0637\u064a \u0647\u0648 \u0637\u0631\u064a\u0642\u0629 \u064a\u0645\u0643\u0646\u0643 \u0627\u0633\u062a\u062e\u062f\u0627\u0645\u0647\u0627 \u0639\u0646\u062f\u0645\u0627 \u064a\u0643\u0648\u0646 \u0644\u062f\u064a\u0643 \u0645\u062c\u0645\u0648\u0639\u0629 \u0645\u0646 \u0627\u0644\u0645\u062a\u063a\u064a\u0631\u0627\u062a \u0627\u0644\u0645\u062a\u0648\u0642\u0639\u0629 \u0648\u062a\u0631\u063a\u0628 \u0641\u064a \u062a\u0635\u0646\u064a\u0641 \u0645\u062a\u063a\u064a\u0631 \u0627\u0644\u0627\u0633\u062a\u062c\u0627\u0628\u0629 \u0625\u0644\u0649 \u0641\u0626\u062a\u064a\u0646 \u0623\u0648 \u0623\u0643\u062b\u0631. \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 \u0644\u0643\u064a\u0641\u064a\u0629 \u0625\u062c\u0631\u0627\u0621 \u0627\u0644\u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a \u0627\u0644\u062e\u0637\u064a \u0641\u064a \u0628\u0627\u064a\u062b\u0648\u0646. \u0627\u0644\u062e\u0637\u0648\u0629 1: \u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u0645\u0643\u062a\u0628\u0627\u062a \u0627\u0644\u0636\u0631\u0648\u0631\u064a\u0629 \u0623\u0648\u0644\u0627\u064b\u060c \u0633\u0646\u0642\u0648\u0645 \u0628\u062a\u062d\u0645\u064a\u0644 \u0627\u0644\u0648\u0638\u0627\u0626\u0641 \u0648\u0627\u0644\u0645\u0643\u062a\u0628\u0627\u062a \u0627\u0644\u0644\u0627\u0632\u0645\u0629 \u0644\u0647\u0630\u0627 \u0627\u0644\u0645\u062b\u0627\u0644: from sklearn. model_selection import train_test_split [&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\u0644\u062a\u062d\u0644\u064a\u0644 \u0627\u0644\u062a\u0645\u064a\u064a\u0632\u064a \u0627\u0644\u062e\u0637\u064a \u0641\u064a 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