{"id":1171,"date":"2023-07-27T09:53:30","date_gmt":"2023-07-27T09:53:30","guid":{"rendered":"https:\/\/statorials.org\/uk\/%d0%b2%d0%b8%d0%bf%d1%83%d1%81%d1%82%d1%96%d1%82%d1%8c-%d0%bf%d0%b5%d1%80%d0%b5%d1%85%d1%80%d0%b5%d1%81%d0%bd%d1%83-%d0%bf%d0%b5%d1%80%d0%b5%d0%b2%d1%96%d1%80%d0%ba%d1%83-%d0%b2-python\/"},"modified":"2023-07-27T09:53:30","modified_gmt":"2023-07-27T09:53:30","slug":"%d0%b2%d0%b8%d0%bf%d1%83%d1%81%d1%82%d1%96%d1%82%d1%8c-%d0%bf%d0%b5%d1%80%d0%b5%d1%85%d1%80%d0%b5%d1%81%d0%bd%d1%83-%d0%bf%d0%b5%d1%80%d0%b5%d0%b2%d1%96%d1%80%d0%ba%d1%83-%d0%b2-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/uk\/%d0%b2%d0%b8%d0%bf%d1%83%d1%81%d1%82%d1%96%d1%82%d1%8c-%d0%bf%d0%b5%d1%80%d0%b5%d1%85%d1%80%d0%b5%d1%81%d0%bd%d1%83-%d0%bf%d0%b5%d1%80%d0%b5%d0%b2%d1%96%d1%80%d0%ba%d1%83-%d0%b2-python\/","title":{"rendered":"\u041f\u0435\u0440\u0435\u0445\u0440\u0435\u0441\u043d\u0430 \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0430 leave-one-out \u0443 python (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)"},"content":{"rendered":"

<\/p>\n

\n

\u0429\u043e\u0431 \u043e\u0446\u0456\u043d\u0438\u0442\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u0456\u0441\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0456 \u043d\u0430 \u043d\u0430\u0431\u043e\u0440\u0456 \u0434\u0430\u043d\u0438\u0445, \u043d\u0430\u043c \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0432\u0438\u043c\u0456\u0440\u044f\u0442\u0438, \u043d\u0430\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438, \u0437\u0440\u043e\u0431\u043b\u0435\u043d\u0456 \u043c\u043e\u0434\u0435\u043b\u043b\u044e, \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u044e\u0442\u044c \u0434\u0430\u043d\u0438\u043c \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f.<\/span><\/p>\n

\u0417\u0430\u0437\u0432\u0438\u0447\u0430\u0439 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u043d\u0438\u0439 \u043c\u0435\u0442\u043e\u0434 \u0434\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u0432\u0456\u0434\u043e\u043c\u0438\u0439 \u044f\u043a Leave-One-Out Cross-Validation (LOOCV)<\/a> , \u044f\u043a\u0438\u0439 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u0442\u0430\u043a\u0438\u0439 \u043f\u0456\u0434\u0445\u0456\u0434:<\/span><\/p>\n

1.<\/strong> \u0420\u043e\u0437\u0434\u0456\u043b\u0456\u0442\u044c \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0456 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 \u043d\u0430\u0431\u0456\u0440, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0432\u0441\u0456 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u043a\u0440\u0456\u043c \u043e\u0434\u043d\u043e\u0433\u043e, \u044f\u043a \u0447\u0430\u0441\u0442\u0438\u043d\u0443 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443.<\/span><\/p>\n

2.<\/strong> \u0421\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u044c, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u043b\u0438\u0448\u0435 \u0434\u0430\u043d\u0456 \u0437 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u043e\u0457 \u043c\u043d\u043e\u0436\u0438\u043d\u0438.<\/span><\/p>\n

3.<\/strong> \u0412\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0439\u0442\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u043b\u044f \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0443\u0432\u0430\u043d\u043d\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0456\u0434\u0433\u0443\u043a\u0443 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u0432\u0438\u043a\u043b\u044e\u0447\u0435\u043d\u043e\u0433\u043e \u0437 \u043c\u043e\u0434\u0435\u043b\u0456, \u0456 \u043e\u0431\u0447\u0438\u0441\u043b\u0456\u0442\u044c \u0441\u0435\u0440\u0435\u0434\u043d\u044e \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u0443 \u043f\u043e\u043c\u0438\u043b\u043a\u0443 (MSE).<\/span><\/p>\n

4.<\/strong> \u041f\u043e\u0432\u0442\u043e\u0440\u0456\u0442\u044c \u0446\u0435\u0439 \u043f\u0440\u043e\u0446\u0435\u0441 n<\/em> \u0440\u0430\u0437\u0456\u0432. \u041e\u0431\u0447\u0438\u0441\u043b\u0456\u0442\u044c \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 MSE \u044f\u043a \u0441\u0435\u0440\u0435\u0434\u043d\u0454 \u0437\u043d\u0430\u0447\u0435\u043d\u043d\u044f \u0432\u0441\u0456\u0445 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0445 MSE.<\/span><\/p>\n

\u0426\u0435\u0439 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a \u043d\u0430\u0434\u0430\u0454 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434 \u0442\u043e\u0433\u043e, \u044f\u043a \u0437\u0430\u043f\u0443\u0441\u0442\u0438\u0442\u0438 LOOCV \u0434\u043b\u044f \u0434\u0430\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456 \u0432 Python.<\/span><\/p>\n

\u041a\u0440\u043e\u043a 1: \u0417\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0442\u0435 \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0456 \u0431\u0456\u0431\u043b\u0456\u043e\u0442\u0435\u043a\u0438<\/strong><\/span><\/h3>\n

\u0421\u043f\u043e\u0447\u0430\u0442\u043a\u0443 \u043c\u0438 \u0437\u0430\u0432\u0430\u043d\u0442\u0430\u0436\u0438\u043c\u043e \u0444\u0443\u043d\u043a\u0446\u0456\u0457 \u0442\u0430 \u0431\u0456\u0431\u043b\u0456\u043e\u0442\u0435\u043a\u0438, \u043d\u0435\u043e\u0431\u0445\u0456\u0434\u043d\u0456 \u0434\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0443:<\/span><\/p>\n

 from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> LeaveOneOut\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> cross_val_score\nfrom<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\nfrom<\/span> numpy import<\/span> means\nfrom<\/span> numpy import<\/span> absolute\nfrom<\/span> numpy import<\/span> sqrt\nimport<\/span> pandas as<\/span> pd\n<\/strong><\/span><\/pre>\n
 \u041a\u0440\u043e\u043a 2: \u0421\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u0434\u0430\u043d\u0456<\/strong><\/span><\/h3>\n
 \u0414\u0430\u043b\u0456 \u043c\u0438 \u0441\u0442\u0432\u043e\u0440\u0438\u043c\u043e pandas DataFrame, \u044f\u043a\u0438\u0439 \u043c\u0456\u0441\u0442\u0438\u0442\u044c \u0434\u0432\u0456 \u0437\u043c\u0456\u043d\u043d\u0456 \u043f\u0440\u0435\u0434\u0438\u043a\u0442\u043e\u0440\u0430, x1<\/sub> \u0456 x2<\/sub> , \u0456 \u043e\u0434\u043d\u0443 \u0437\u043c\u0456\u043d\u043d\u0443 \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0456 y.<\/span><\/p>\n
 df = pd.DataFrame({' y<\/span> ': [6, 8, 12, 14, 14, 15, 17, 22, 24, 23],\n                   ' x1<\/span> ': [2, 5, 4, 3, 4, 6, 7, 5, 8, 9],\n                   ' x2<\/span> ': [14, 12, 12, 13, 7, 8, 7, 4, 6, 5]})\n<\/strong><\/span><\/pre>\n
 \u041a\u0440\u043e\u043a 3: \u0412\u0438\u043a\u043e\u043d\u0430\u0439\u0442\u0435 \u043f\u0435\u0440\u0435\u0445\u0440\u0435\u0441\u043d\u0443 \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0443 Leave-One-Out<\/strong><\/span><\/h3>\n
 \u0414\u0430\u043b\u0456 \u043c\u0438 \u043f\u0456\u0434\u0431\u0435\u0440\u0435\u043c\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u043c\u043d\u043e\u0436\u0438\u043d\u043d\u043e\u0457 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457<\/a> \u0434\u043e \u043d\u0430\u0431\u043e\u0440\u0443 \u0434\u0430\u043d\u0438\u0445 \u0456 \u0432\u0438\u043a\u043e\u043d\u0430\u0454\u043c\u043e LOOCV, \u0449\u043e\u0431 \u043e\u0446\u0456\u043d\u0438\u0442\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u0456\u0441\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0456.<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv = LeaveOneOut()\n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use LOOCV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_absolute_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view mean absolute error\n<\/span>mean(absolute(scores))\n\n3.1461548083469726\n<\/strong><\/span><\/pre>\n
 \u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0441\u0435\u0440\u0435\u0434\u043d\u044f \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u0430 \u043f\u043e\u0445\u0438\u0431\u043a\u0430 (MAE) \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043b\u0430 3,146<\/strong> . \u0422\u043e\u0431\u0442\u043e \u0441\u0435\u0440\u0435\u0434\u043d\u044f \u0430\u0431\u0441\u043e\u043b\u044e\u0442\u043d\u0430 \u043f\u043e\u0445\u0438\u0431\u043a\u0430 \u043c\u0456\u0436 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u043e\u043c \u043c\u043e\u0434\u0435\u043b\u0456 \u0442\u0430 \u0444\u0430\u043a\u0442\u0438\u0447\u043d\u043e \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0443\u0432\u0430\u043d\u0438\u043c\u0438 \u0434\u0430\u043d\u0438\u043c\u0438 \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c 3,146.<\/span><\/p>\n
 \u0417\u0430\u0433\u0430\u043b\u043e\u043c, \u0447\u0438\u043c \u043d\u0438\u0436\u0447\u0435 MAE, \u0442\u0438\u043c \u043a\u0440\u0430\u0449\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0437\u0434\u0430\u0442\u043d\u0430 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u0442\u0438 \u0444\u0430\u043a\u0442\u0438\u0447\u043d\u0456 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f.<\/span><\/p>\n
 \u0406\u043d\u0448\u0438\u043c \u0447\u0430\u0441\u0442\u043e \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u043d\u0438\u043c \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a\u043e\u043c \u0434\u043b\u044f \u043e\u0446\u0456\u043d\u043a\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u043e\u0441\u0442\u0456 \u043c\u043e\u0434\u0435\u043b\u0456 \u0454 \u0441\u0435\u0440\u0435\u0434\u043d\u044c\u043e\u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u0430 \u043f\u043e\u043c\u0438\u043b\u043a\u0430 (RMSE). \u041d\u0430\u0441\u0442\u0443\u043f\u043d\u0438\u0439 \u043a\u043e\u0434 \u043f\u043e\u043a\u0430\u0437\u0443\u0454, \u044f\u043a \u043e\u0431\u0447\u0438\u0441\u043b\u0438\u0442\u0438 \u0446\u0435\u0439 \u043f\u043e\u043a\u0430\u0437\u043d\u0438\u043a \u0437\u0430 \u0434\u043e\u043f\u043e\u043c\u043e\u0433\u043e\u044e LOOCV:<\/span><\/p>\n
 #define predictor and response variables\n<\/span>X = df[[' x1<\/span> ', ' x2<\/span> ']]\ny = df[' y<\/span> ']\n\n#define cross-validation method to use\n<\/span>cv = LeaveOneOut()\n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use LOOCV to evaluate model\n<\/span>scores = cross_val_score(model, X, y, scoring=' neg_mean_squared_error<\/span> ',\n                         cv=cv, n_jobs=-1)\n\n#view RMSE\n<\/span>sqrt(mean(absolute(scores)))\n\n3.619456476385567<\/strong><\/span><\/pre>\n
 \u0417 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u0443 \u043c\u0438 \u0431\u0430\u0447\u0438\u043c\u043e, \u0449\u043e \u0441\u0435\u0440\u0435\u0434\u043d\u044f \u043a\u0432\u0430\u0434\u0440\u0430\u0442\u0438\u0447\u043d\u0430 \u043f\u043e\u043c\u0438\u043b\u043a\u0430 (RMSE) \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u043b\u0430 3,619<\/strong> . \u0427\u0438\u043c \u043d\u0438\u0436\u0447\u0435 RMSE, \u0442\u0438\u043c \u043a\u0440\u0430\u0449\u0435 \u043c\u043e\u0434\u0435\u043b\u044c \u0437\u0434\u0430\u0442\u043d\u0430 \u043f\u0435\u0440\u0435\u0434\u0431\u0430\u0447\u0438\u0442\u0438 \u0444\u0430\u043a\u0442\u0438\u0447\u043d\u0456 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f.<\/span><\/p>\n
 \u041d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u0446\u0456 \u043c\u0438 \u0437\u0430\u0437\u0432\u0438\u0447\u0430\u0439 \u043f\u0456\u0434\u0431\u0438\u0440\u0430\u0454\u043c\u043e \u043a\u0456\u043b\u044c\u043a\u0430 \u0440\u0456\u0437\u043d\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0456 \u043f\u043e\u0440\u0456\u0432\u043d\u044e\u0454\u043c\u043e RMSE \u0430\u0431\u043e MAE \u043a\u043e\u0436\u043d\u043e\u0457 \u043c\u043e\u0434\u0435\u043b\u0456, \u0449\u043e\u0431 \u0432\u0438\u0440\u0456\u0448\u0438\u0442\u0438, \u044f\u043a\u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u0434\u0430\u0454 \u043d\u0430\u0439\u043d\u0438\u0436\u0447\u0438\u0439 \u0440\u0456\u0432\u0435\u043d\u044c \u043f\u043e\u043c\u0438\u043b\u043e\u043a \u0442\u0435\u0441\u0442\u0443\u0432\u0430\u043d\u043d\u044f \u0456, \u043e\u0442\u0436\u0435, \u0454 \u043d\u0430\u0439\u043a\u0440\u0430\u0449\u043e\u044e \u0434\u043b\u044f \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u0430\u043d\u043d\u044f.<\/span><\/p>\n
 \u0414\u043e\u0434\u0430\u0442\u043a\u043e\u0432\u0456 \u0440\u0435\u0441\u0443\u0440\u0441\u0438<\/strong><\/span><\/h3>\n
 \u041a\u043e\u0440\u043e\u0442\u043a\u0438\u0439 \u0432\u0441\u0442\u0443\u043f \u0434\u043e \u043f\u0435\u0440\u0435\u0445\u0440\u0435\u0441\u043d\u043e\u0457 \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0438 Leave-One-Out (LOOCV)<\/a>
 \u041f\u043e\u0432\u043d\u0438\u0439 \u043f\u043e\u0441\u0456\u0431\u043d\u0438\u043a \u0456\u0437 \u043b\u0456\u043d\u0456\u0439\u043d\u043e\u0457 \u0440\u0435\u0433\u0440\u0435\u0441\u0456\u0457 \u0432 Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u0429\u043e\u0431 \u043e\u0446\u0456\u043d\u0438\u0442\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u0438\u0432\u043d\u0456\u0441\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u0456 \u043d\u0430 \u043d\u0430\u0431\u043e\u0440\u0456 \u0434\u0430\u043d\u0438\u0445, \u043d\u0430\u043c \u043f\u043e\u0442\u0440\u0456\u0431\u043d\u043e \u0432\u0438\u043c\u0456\u0440\u044f\u0442\u0438, \u043d\u0430\u0441\u043a\u0456\u043b\u044c\u043a\u0438 \u043f\u0440\u043e\u0433\u043d\u043e\u0437\u0438, \u0437\u0440\u043e\u0431\u043b\u0435\u043d\u0456 \u043c\u043e\u0434\u0435\u043b\u043b\u044e, \u0432\u0456\u0434\u043f\u043e\u0432\u0456\u0434\u0430\u044e\u0442\u044c \u0434\u0430\u043d\u0438\u043c \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f. \u0417\u0430\u0437\u0432\u0438\u0447\u0430\u0439 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0432\u0430\u043d\u0438\u0439 \u043c\u0435\u0442\u043e\u0434 \u0434\u043b\u044f \u0446\u044c\u043e\u0433\u043e \u0432\u0456\u0434\u043e\u043c\u0438\u0439 \u044f\u043a Leave-One-Out Cross-Validation (LOOCV) , \u044f\u043a\u0438\u0439 \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u0454 \u0442\u0430\u043a\u0438\u0439 \u043f\u0456\u0434\u0445\u0456\u0434: 1. \u0420\u043e\u0437\u0434\u0456\u043b\u0456\u0442\u044c \u043d\u0430\u0431\u0456\u0440 \u0434\u0430\u043d\u0438\u0445 \u043d\u0430 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u0438\u0439 \u043d\u0430\u0431\u0456\u0440 \u0456 \u0442\u0435\u0441\u0442\u043e\u0432\u0438\u0439 \u043d\u0430\u0431\u0456\u0440, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 \u0432\u0441\u0456 \u0441\u043f\u043e\u0441\u0442\u0435\u0440\u0435\u0436\u0435\u043d\u043d\u044f, \u043a\u0440\u0456\u043c \u043e\u0434\u043d\u043e\u0433\u043e, \u044f\u043a \u0447\u0430\u0441\u0442\u0438\u043d\u0443 \u043d\u0430\u0432\u0447\u0430\u043b\u044c\u043d\u043e\u0433\u043e \u043d\u0430\u0431\u043e\u0440\u0443. 2. \u0421\u0442\u0432\u043e\u0440\u0456\u0442\u044c \u043c\u043e\u0434\u0435\u043b\u044c, \u0432\u0438\u043a\u043e\u0440\u0438\u0441\u0442\u043e\u0432\u0443\u044e\u0447\u0438 […]<\/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":[],"yoast_head":"\n\u041f\u0435\u0440\u0435\u0445\u0440\u0435\u0441\u043d\u0430 \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0430 Leave-One-Out \u0443 Python (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)<\/title>\n<meta name=\"description\" content=\"\u0423 \u0446\u044c\u043e\u043c\u0443 \u043f\u0456\u0434\u0440\u0443\u0447\u043d\u0438\u043a\u0443 \u043f\u043e\u044f\u0441\u043d\u044e\u0454\u0442\u044c\u0441\u044f, \u044f\u043a \u0432\u0438\u043a\u043e\u043d\u0443\u0432\u0430\u0442\u0438 \u043f\u0435\u0440\u0435\u0445\u0440\u0435\u0441\u043d\u0443 \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0443 \u0432 Python, \u0432\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u0438 \u043f\u043e\u043a\u0440\u043e\u043a\u043e\u0432\u0438\u0439 \u043f\u0440\u0438\u043a\u043b\u0430\u0434.\" \/>\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\/uk\/\u0432\u0438\u043f\u0443\u0441\u0442\u0456\u0442\u044c-\u043f\u0435\u0440\u0435\u0445\u0440\u0435\u0441\u043d\u0443-\u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0443-\u0432-python\/\" \/>\n<meta property=\"og:locale\" content=\"uk_UA\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u041f\u0435\u0440\u0435\u0445\u0440\u0435\u0441\u043d\u0430 \u043f\u0435\u0440\u0435\u0432\u0456\u0440\u043a\u0430 Leave-One-Out \u0443 Python (\u0437 \u043f\u0440\u0438\u043a\u043b\u0430\u0434\u0430\u043c\u0438)\" \/>\n<meta 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