{"id":1162,"date":"2023-07-27T10:43:39","date_gmt":"2023-07-27T10:43:39","guid":{"rendered":"https:\/\/statorials.org\/ru\/%d0%bb%d0%b8%d0%bd%d0%b5%d0%b8%d0%bd%d1%8b%d0%b8-%d0%b4%d0%b8%d1%81%d0%ba%d1%80%d0%b8%d0%bc%d0%b8%d0%bd%d0%b0%d0%bd%d1%82%d0%bd%d1%8b%d0%b8-%d0%b0%d0%bd%d0%b0%d0%bb%d0%b8%d0%b7-%d0%b2-python\/"},"modified":"2023-07-27T10:43:39","modified_gmt":"2023-07-27T10:43:39","slug":"%d0%bb%d0%b8%d0%bd%d0%b5%d0%b8%d0%bd%d1%8b%d0%b8-%d0%b4%d0%b8%d1%81%d0%ba%d1%80%d0%b8%d0%bc%d0%b8%d0%bd%d0%b0%d0%bd%d1%82%d0%bd%d1%8b%d0%b8-%d0%b0%d0%bd%d0%b0%d0%bb%d0%b8%d0%b7-%d0%b2-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/ru\/%d0%bb%d0%b8%d0%bd%d0%b5%d0%b8%d0%bd%d1%8b%d0%b8-%d0%b4%d0%b8%d1%81%d0%ba%d1%80%d0%b8%d0%bc%d0%b8%d0%bd%d0%b0%d0%bd%d1%82%d0%bd%d1%8b%d0%b8-%d0%b0%d0%bd%d0%b0%d0%bb%d0%b8%d0%b7-%d0%b2-python\/","title":{"rendered":"\u041b\u0438\u043d\u0435\u0439\u043d\u044b\u0439 \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u0432 python (\u0448\u0430\u0433 \u0437\u0430 \u0448\u0430\u0433\u043e\u043c)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/ru\/\u043b\u0438\u043d\u0435\u0438\u043d\u044b\u0438-\u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u044b\u0438-\u0430\u043d\u0430\u043b\u0438\u0437\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u041b\u0438\u043d\u0435\u0439\u043d\u044b\u0439 \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437<\/a> \u2014 \u044d\u0442\u043e \u043c\u0435\u0442\u043e\u0434, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c, \u043a\u043e\u0433\u0434\u0430 \u0443 \u0432\u0430\u0441 \u0435\u0441\u0442\u044c \u043d\u0430\u0431\u043e\u0440 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445-\u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u043e\u0432 \u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u0442\u044c <a href=\"https:\/\/statorials.org\/ru\/\u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435-\u043f\u043e\u044f\u0441\u043d\u044f\u044e\u0449\u0438\u0435-\u043e\u0442\u0432\u0435\u0442\u044b\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e \u043e\u0442\u0432\u0435\u0442\u0430<\/a> \u043d\u0430 \u0434\u0432\u0430 \u0438\u043b\u0438 \u0431\u043e\u043b\u0435\u0435 \u043a\u043b\u0430\u0441\u0441\u043e\u0432.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u043f\u043e\u0448\u0430\u0433\u043e\u0432\u044b\u0439 \u043f\u0440\u0438\u043c\u0435\u0440 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0433\u043e \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u043e\u0433\u043e \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0432 Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0428\u0430\u0433 1. \u0417\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u043c\u044b \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u043c \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u0438 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u043f\u0440\u0438\u043c\u0435\u0440\u0430:<\/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> <span style=\"color: #000000;\"><strong>\u0428\u0430\u0433 2. \u0417\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u0435 \u0434\u0430\u043d\u043d\u044b\u0435<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0412 \u044d\u0442\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445 \u0440\u0430\u0434\u0443\u0436\u043d\u043e\u0439 \u043e\u0431\u043e\u043b\u043e\u0447\u043a\u0438 <strong>\u0433\u043b\u0430\u0437\u0430<\/strong> \u0438\u0437 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 sklearn. \u0421\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u043a\u0430\u043a \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u044c \u044d\u0442\u043e\u0442 \u043d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438 \u043f\u0440\u0435\u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u0442\u044c \u0435\u0433\u043e \u0432 DataFrame pandas \u0434\u043b\u044f \u043f\u0440\u043e\u0441\u0442\u043e\u0442\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f:<\/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> <span style=\"color: #000000;\">\u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u043d\u0430\u0431\u043e\u0440 \u0434\u0430\u043d\u043d\u044b\u0445 \u0441\u043e\u0434\u0435\u0440\u0436\u0438\u0442 \u0432\u0441\u0435\u0433\u043e 150 \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u0439.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0412 \u044d\u0442\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043c\u044b \u043f\u043e\u0441\u0442\u0440\u043e\u0438\u043c \u043c\u043e\u0434\u0435\u043b\u044c \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0433\u043e \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u043e\u0433\u043e \u0430\u043d\u0430\u043b\u0438\u0437\u0430, \u0447\u0442\u043e\u0431\u044b \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u0442\u044c, \u043a \u043a\u0430\u043a\u043e\u043c\u0443 \u0432\u0438\u0434\u0443 \u043f\u0440\u0438\u043d\u0430\u0434\u043b\u0435\u0436\u0438\u0442 \u0434\u0430\u043d\u043d\u044b\u0439 \u0446\u0432\u0435\u0442\u043e\u043a.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0412 \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0435-\u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u044b:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u0414\u043b\u0438\u043d\u0430 \u0447\u0430\u0448\u0435\u043b\u0438\u0441\u0442\u0438\u043a\u0430<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0428\u0438\u0440\u0438\u043d\u0430 \u0447\u0430\u0448\u0435\u043b\u0438\u0441\u0442\u0438\u043a\u0430<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0414\u043b\u0438\u043d\u0430 \u043b\u0435\u043f\u0435\u0441\u0442\u043a\u0430<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0428\u0438\u0440\u0438\u043d\u0430 \u043b\u0435\u043f\u0435\u0441\u0442\u043a\u0430<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">\u0418 \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0438\u0445 \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u043e\u0439 \u043e\u0442\u0432\u0435\u0442\u0430 <em>\u0432\u0438\u0434\u0430<\/em> , \u043a\u043e\u0442\u043e\u0440\u0430\u044f \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u0442 \u0441\u043b\u0435\u0434\u0443\u044e\u0449\u0438\u0435 \u0442\u0440\u0438 \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0445 \u043a\u043b\u0430\u0441\u0441\u0430:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">\u0441\u0435\u0442\u043e\u0437\u0430<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u043b\u0438\u0448\u0430\u0439<\/span><\/li>\n<li> <span style=\"color: #000000;\">\u0412\u0438\u0440\u0434\u0436\u0438\u043d\u0438\u044f<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>\u0428\u0430\u0433 3. \u041d\u0430\u0441\u0442\u0440\u043e\u0439\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c LDA<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u0414\u0430\u043b\u0435\u0435 \u043c\u044b \u043f\u043e\u0434\u0433\u043e\u043d\u0438\u043c \u043c\u043e\u0434\u0435\u043b\u044c LDA \u043a \u043d\u0430\u0448\u0438\u043c \u0434\u0430\u043d\u043d\u044b\u043c \u0441 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u0438 <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html\" target=\"_blank\" rel=\"noopener noreferrer\">LinearDiscrimiantAnalsys<\/a> 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> <span style=\"color: #000000;\"><strong>\u0428\u0430\u0433&nbsp;4. \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041f\u043e\u0441\u043b\u0435 \u0442\u043e\u0433\u043e, \u043a\u0430\u043a \u043c\u044b \u043f\u043e\u0434\u043e\u0433\u043d\u0430\u043b\u0438 \u043c\u043e\u0434\u0435\u043b\u044c, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u043d\u0430\u0448\u0438 \u0434\u0430\u043d\u043d\u044b\u0435, \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0438, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044f \u043f\u043e\u0432\u0442\u043e\u0440\u043d\u0443\u044e \u0441\u0442\u0440\u0430\u0442\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u0443\u044e k-\u043a\u0440\u0430\u0442\u043d\u0443\u044e \u043f\u0435\u0440\u0435\u043a\u0440\u0435\u0441\u0442\u043d\u0443\u044e \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0443.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u0412 \u044d\u0442\u043e\u043c \u043f\u0440\u0438\u043c\u0435\u0440\u0435 \u043c\u044b \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c 10 \u0441\u043a\u043b\u0430\u0434\u043e\u043a \u0438 3 \u043f\u043e\u0432\u0442\u043e\u0440\u0435\u043d\u0438\u044f:<\/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> <span style=\"color: #000000;\">\u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043e\u0441\u0442\u0438\u0433\u043b\u0430 \u0441\u0440\u0435\u0434\u043d\u0435\u0439 \u0442\u043e\u0447\u043d\u043e\u0441\u0442\u0438 <strong>97,78%<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">\u041c\u044b \u0442\u0430\u043a\u0436\u0435 \u043c\u043e\u0436\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u044c, \u0447\u0442\u043e\u0431\u044b \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u0430\u0442\u044c, \u043a \u043a\u0430\u043a\u043e\u043c\u0443 \u043a\u043b\u0430\u0441\u0441\u0443 \u043f\u0440\u0438\u043d\u0430\u0434\u043b\u0435\u0436\u0438\u0442 \u043d\u043e\u0432\u044b\u0439 \u0446\u0432\u0435\u0442\u043e\u043a, \u043d\u0430 \u043e\u0441\u043d\u043e\u0432\u0435 \u0432\u0445\u043e\u0434\u043d\u044b\u0445 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439:<\/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> <span style=\"color: #000000;\">\u041c\u044b \u0432\u0438\u0434\u0438\u043c, \u0447\u0442\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043f\u0440\u0435\u0434\u0441\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442, \u0447\u0442\u043e \u044d\u0442\u043e \u043d\u043e\u0432\u043e\u0435 \u043d\u0430\u0431\u043b\u044e\u0434\u0435\u043d\u0438\u0435 \u043f\u0440\u0438\u043d\u0430\u0434\u043b\u0435\u0436\u0438\u0442 \u0432\u0438\u0434\u0443, \u043d\u0430\u0437\u044b\u0432\u0430\u0435\u043c\u043e\u043c\u0443 <em>setosa<\/em> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\u0428\u0430\u0433 5: \u0412\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0438\u0440\u0443\u0439\u0442\u0435 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u044b<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">\u041d\u0430\u043a\u043e\u043d\u0435\u0446, \u043c\u044b \u043c\u043e\u0436\u0435\u043c \u0441\u043e\u0437\u0434\u0430\u0442\u044c \u0433\u0440\u0430\u0444\u0438\u043a LDA, \u0447\u0442\u043e\u0431\u044b \u0432\u0438\u0437\u0443\u0430\u043b\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043b\u0438\u043d\u0435\u0439\u043d\u044b\u0435 \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u044b \u043c\u043e\u0434\u0435\u043b\u0438 \u0438 \u043f\u043e\u043d\u044f\u0442\u044c, \u043d\u0430\u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0445\u043e\u0440\u043e\u0448\u043e \u043e\u043d\u0430 \u0440\u0430\u0437\u0434\u0435\u043b\u044f\u0435\u0442 \u0442\u0440\u0438 \u0440\u0430\u0437\u043d\u044b\u0445 \u0432\u0438\u0434\u0430 \u0432 \u043d\u0430\u0448\u0435\u043c \u043d\u0430\u0431\u043e\u0440\u0435 \u0434\u0430\u043d\u043d\u044b\u0445:<\/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><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-11651 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/ldapython1.png\" alt=\"\u043b\u0438\u043d\u0435\u0439\u043d\u044b\u0439 \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u0432 Python\" width=\"416\" height=\"281\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p> <span style=\"color: #000000;\">\u041f\u043e\u043b\u043d\u044b\u0439 \u043a\u043e\u0434 Python, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u044b\u0439 \u0432 \u044d\u0442\u043e\u043c \u0443\u0440\u043e\u043a\u0435, \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u043d\u0430\u0439\u0442\u0438 <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/linear_discriminant_analysis\" target=\"_blank\" rel=\"noopener noreferrer\">\u0437\u0434\u0435\u0441\u044c<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u041b\u0438\u043d\u0435\u0439\u043d\u044b\u0439 \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u2014 \u044d\u0442\u043e \u043c\u0435\u0442\u043e\u0434, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c, \u043a\u043e\u0433\u0434\u0430 \u0443 \u0432\u0430\u0441 \u0435\u0441\u0442\u044c \u043d\u0430\u0431\u043e\u0440 \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u044b\u0445-\u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u043e\u0432 \u0438 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u0446\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043f\u0435\u0440\u0435\u043c\u0435\u043d\u043d\u0443\u044e \u043e\u0442\u0432\u0435\u0442\u0430 \u043d\u0430 \u0434\u0432\u0430 \u0438\u043b\u0438 \u0431\u043e\u043b\u0435\u0435 \u043a\u043b\u0430\u0441\u0441\u043e\u0432. \u0412 \u044d\u0442\u043e\u043c \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0441\u0442\u0432\u0435 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d \u043f\u043e\u0448\u0430\u0433\u043e\u0432\u044b\u0439 \u043f\u0440\u0438\u043c\u0435\u0440 \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u044f \u043b\u0438\u043d\u0435\u0439\u043d\u043e\u0433\u043e \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u043e\u0433\u043e \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0432 Python. \u0428\u0430\u0433 1. \u0417\u0430\u0433\u0440\u0443\u0437\u0438\u0442\u0435 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0421\u043d\u0430\u0447\u0430\u043b\u0430 \u043c\u044b \u0437\u0430\u0433\u0440\u0443\u0437\u0438\u043c \u0444\u0443\u043d\u043a\u0446\u0438\u0438 \u0438 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u043b\u044f \u044d\u0442\u043e\u0433\u043e \u043f\u0440\u0438\u043c\u0435\u0440\u0430: from [&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":[],"class_list":["post-1162","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\u041b\u0438\u043d\u0435\u0439\u043d\u044b\u0439 \u0434\u0438\u0441\u043a\u0440\u0438\u043c\u0438\u043d\u0430\u043d\u0442\u043d\u044b\u0439 \u0430\u043d\u0430\u043b\u0438\u0437 \u0432 Python 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