{"id":1174,"date":"2023-07-27T09:53:30","date_gmt":"2023-07-27T09:53:30","guid":{"rendered":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-cross-validation-%e1%80%80%e1%80%ad%e1%80%af-%e1%80%91%e1%80%af%e1%80%90%e1%80%ba%e1%80%95%e1%80%b1%e1%80%b8%e1%80%95%e1%80%ab%e1%81%8b\/"},"modified":"2023-07-27T09:53:30","modified_gmt":"2023-07-27T09:53:30","slug":"python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-cross-validation-%e1%80%80%e1%80%ad%e1%80%af-%e1%80%91%e1%80%af%e1%80%90%e1%80%ba%e1%80%95%e1%80%b1%e1%80%b8%e1%80%95%e1%80%ab%e1%81%8b","status":"publish","type":"post","link":"https:\/\/statorials.org\/my\/python-%e1%80%90%e1%80%bd%e1%80%84%e1%80%ba-cross-validation-%e1%80%80%e1%80%ad%e1%80%af-%e1%80%91%e1%80%af%e1%80%90%e1%80%ba%e1%80%95%e1%80%b1%e1%80%b8%e1%80%95%e1%80%ab%e1%81%8b\/","title":{"rendered":"Python \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1001\u102f\u1010\u100a\u103a\u1038-\u1021\u1011\u103d\u1000\u103a cross-validation (\u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1021\u1010\u1030)"},"content":{"rendered":"

<\/p>\n

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\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u1015\u1031\u102b\u103a\u101b\u103e\u102d \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u104f\u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u1000\u102d\u102f \u1021\u1000\u1032\u1016\u103c\u1010\u103a\u101b\u1014\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103e\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101e\u1031\u102c \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u101c\u1031\u1037\u101c\u102c\u1011\u102c\u1038\u101e\u1031\u102c\u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1019\u100a\u103a\u1019\u103b\u103e\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1010\u102d\u102f\u1004\u103a\u1038\u1010\u102c\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u1015\u102b\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1024\u1000\u1032\u1037\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a \u1021\u101e\u102f\u1036\u1038\u1019\u103b\u102c\u1038\u101e\u1031\u102c\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f Leave-One-Out Cross-Validation (LOOCV)<\/a> \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a-<\/span><\/p>\n

1.<\/strong> \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u104f \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1016\u103c\u1005\u103a \u1019\u103e\u1010\u103a\u101e\u102c\u1038\u1019\u103e\u102f\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101c\u102d\u102f\u1000\u103a\u1015\u102b\u104b<\/span><\/p>\n

2.<\/strong> \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036\u1019\u103e\u1012\u1031\u1010\u102c\u1000\u102d\u102f\u101e\u102c\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b<\/span><\/p>\n

3.<\/strong> \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103e\u1016\u101a\u103a\u1011\u102f\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u1019\u103e\u102f\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1014\u103a\u1014\u103e\u1004\u1037\u103a \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038 (MSE) \u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b<\/span><\/p>\n

4.<\/strong> \u1024\u101c\u102f\u1015\u103a\u1004\u1014\u103a\u1038\u1005\u1009\u103a n<\/em> \u1021\u1000\u103c\u102d\u1019\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u1014\u103a\u101c\u102f\u1015\u103a\u1015\u102b\u104b \u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032 MSE \u1000\u102d\u102f \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f MSE \u1021\u102c\u1038\u101c\u102f\u1036\u1038\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u1016\u103c\u1005\u103a \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1015\u102b\u104b<\/span><\/p>\n

\u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a\u1015\u1031\u1038\u1011\u102c\u1038\u101e\u1031\u102c\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1021\u1010\u103d\u1000\u103a LOOCV \u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102f\u1036\u1021\u1006\u1004\u1037\u103a\u1006\u1004\u1037\u103a\u1000\u102d\u102f \u1025\u1015\u1019\u102c\u1015\u1031\u1038\u1011\u102c\u1038\u101e\u100a\u103a\u104b<\/span><\/p>\n

\u1021\u1006\u1004\u1037\u103a 1- \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c\u1005\u102c\u1000\u103c\u100a\u1037\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n

\u1026\u1038\u1005\u103d\u102c\u104a \u1024\u1025\u1015\u1019\u102c\u1021\u1010\u103d\u1000\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u101e\u1031\u102c \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a \u1012\u1005\u103a\u1002\u103b\u1005\u103a\u1010\u102d\u102f\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1010\u1004\u103a\u1015\u1031\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/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
 \u1021\u1006\u1004\u1037\u103a 2: \u1012\u1031\u1010\u102c\u1000\u102d\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1000\u103c\u102d\u102f\u1010\u1004\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1014\u102d\u102f\u1004\u103a\u101e\u1031\u102c \u1000\u102d\u1014\u103a\u1038\u101b\u103e\u1004\u103a\u1014\u103e\u1005\u103a\u1001\u102f\u1016\u103c\u1005\u103a\u101e\u100a\u1037\u103a x1<\/sub> \u1014\u103e\u1004\u1037\u103a x2<\/sub> \u1014\u103e\u1004\u1037\u103a \u1010\u102f\u1036\u1037\u1015\u103c\u1014\u103a\u1019\u103e\u102f\u1019\u1010\u1030\u100a\u102e\u101e\u100a\u1037\u103a y \u1010\u1005\u103a\u1001\u102f\u1015\u102b\u101b\u103e\u102d\u101e\u1031\u102c pandas DataFrame \u1010\u1005\u103a\u1001\u102f\u1000\u102d\u102f \u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u1019\u100a\u103a\u104b<\/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
 \u1021\u1006\u1004\u1037\u103a 3- Leave-One-Out Cross-Validation \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102b\u104b<\/strong><\/span><\/h3>\n
 \u1011\u102d\u102f\u1037\u1014\u1031\u102c\u1000\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a dataset \u1010\u103d\u1004\u103a multi linear regression model \u1000\u102d\u102f<\/a> \u1016\u103c\u100a\u1037\u103a\u1015\u103c\u102e\u1038 model \u104f \u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u1000\u102d\u102f \u1021\u1000\u1032\u1016\u103c\u1010\u103a\u101b\u1014\u103a LOOCV \u1000\u102d\u102f \u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u1015\u102b\u1019\u100a\u103a\u104b<\/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
 \u101b\u101c\u1012\u103a\u1019\u103e\u104a \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e absolute error (MAE) \u101e\u100a\u103a 3.146<\/strong> \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b \u1006\u102d\u102f\u101c\u102d\u102f\u101e\u100a\u103a\u1019\u103e\u102c\u104a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1014\u103e\u1004\u1037\u103a \u1021\u1019\u103e\u1014\u103a\u1010\u1000\u101a\u103a \u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u101c\u1031\u1037\u101c\u102c\u1011\u102c\u1038\u101e\u100a\u1037\u103a \u1012\u1031\u1010\u102c\u1000\u103c\u102c\u1038\u101b\u103e\u102d \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u1000\u103c\u103d\u1004\u103a\u1038\u1019\u1032\u1037 \u1021\u1019\u103e\u102c\u1038\u101e\u100a\u103a 3.146 \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u101a\u1031\u1018\u1030\u101a\u103b\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a MAE \u1014\u102d\u1019\u1037\u103a\u101c\u1031\u104a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101e\u100a\u103a \u1021\u1019\u103e\u1014\u103a\u1010\u1000\u101a\u103a \u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1014\u102d\u102f\u1004\u103a\u101c\u1031\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u1000\u102d\u102f \u1021\u1000\u1032\u1016\u103c\u1010\u103a\u101b\u1014\u103a \u1014\u1031\u102c\u1000\u103a\u1011\u1015\u103a\u1021\u101e\u102f\u1036\u1038\u1019\u103b\u102c\u1038\u101e\u100a\u1037\u103a \u1019\u1000\u103a\u1011\u101b\u1005\u103a\u1019\u103e\u102c root mean square error (RMSE) \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1000\u102f\u1012\u103a\u101e\u100a\u103a LOOCV \u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1024\u1019\u1000\u103a\u1011\u101b\u1005\u103a\u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1014\u100a\u103a\u1038\u1000\u102d\u102f \u1015\u103c\u101e\u100a\u103a-<\/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
 \u101b\u101c\u1012\u103a\u1019\u103e\u104a root mean square error (RMSE) \u101e\u100a\u103a 3.619<\/strong> \u1016\u103c\u1005\u103a\u101e\u100a\u103a\u1000\u102d\u102f \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u1010\u103d\u1031\u1037\u1019\u103c\u1004\u103a\u1014\u102d\u102f\u1004\u103a\u1015\u102b\u101e\u100a\u103a\u104b RMSE \u1014\u102d\u1019\u1037\u103a\u101c\u1031\u104a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101e\u100a\u103a \u1021\u1019\u103e\u1014\u103a\u1010\u1000\u101a\u103a \u101c\u1031\u1037\u101c\u102c\u1010\u103d\u1031\u1037\u101b\u103e\u102d\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1014\u102d\u102f\u1004\u103a\u101c\u1031\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u101c\u1000\u103a\u1010\u103d\u1031\u1037\u1010\u103d\u1004\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1015\u102f\u1036\u1019\u103e\u1014\u103a\u1021\u102c\u1038\u1016\u103c\u1004\u1037\u103a \u1019\u1010\u1030\u100a\u102e\u101e\u1031\u102c \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103b\u102c\u1038\u1005\u103d\u102c\u1000\u102d\u102f \u1021\u1036\u101d\u1004\u103a\u1001\u103d\u1004\u103a\u1000\u103b\u1016\u103c\u1005\u103a\u1005\u1031\u1015\u103c\u102e\u1038 \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u1005\u102e\u104f RMSE \u101e\u102d\u102f\u1037\u1019\u101f\u102f\u1010\u103a MAE \u1010\u102d\u102f\u1037\u1000\u102d\u102f \u1014\u103e\u102d\u102f\u1004\u103a\u1038\u101a\u103e\u1009\u103a\u1000\u102c \u1019\u100a\u103a\u101e\u100a\u1037\u103a\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u101e\u100a\u103a \u1021\u1014\u102d\u1019\u1037\u103a\u1006\u102f\u1036\u1038\u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f \u1021\u1019\u103e\u102c\u1038\u1021\u101a\u103d\u1004\u103a\u1038\u1014\u103e\u102f\u1014\u103a\u1038\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1011\u102f\u1010\u103a\u101c\u102f\u1015\u103a\u1015\u1031\u1038\u101e\u100a\u103a\u1000\u102d\u102f \u1006\u102f\u1036\u1038\u1016\u103c\u1010\u103a\u101b\u1014\u103a\u1014\u103e\u1004\u1037\u103a \u1011\u102d\u102f\u1037\u1000\u103c\u1031\u102c\u1004\u1037\u103a \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101b\u1014\u103a \u1021\u1000\u1031\u102c\u1004\u103a\u1038\u1006\u102f\u1036\u1038\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1016\u103c\u1005\u103a\u101e\u100a\u103a\u104b<\/span><\/p>\n
 \u1011\u1015\u103a\u101c\u1031\u102c\u1004\u103a\u1038\u1021\u101b\u1004\u103a\u1038\u1021\u1019\u103c\u1005\u103a\u1019\u103b\u102c\u1038<\/strong><\/span><\/h3>\n
 Leave-One-Out Cross-Validation (LOOCV) \u1021\u1019\u103c\u1014\u103a\u1014\u102d\u1012\u102b\u1014\u103a\u1038<\/a>
 Python \u101b\u103e\u102d Linear Regression \u1021\u1010\u103d\u1000\u103a \u1015\u103c\u102e\u1038\u1015\u103c\u100a\u1037\u103a\u1005\u102f\u1036\u101e\u1031\u102c \u101c\u1019\u103a\u1038\u100a\u103d\u103e\u1014\u103a<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\u1012\u1031\u1010\u102c\u1021\u1010\u103d\u1032\u1010\u1005\u103a\u1001\u102f\u1015\u1031\u102b\u103a\u101b\u103e\u102d \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u104f\u1005\u103d\u1019\u103a\u1038\u1006\u1031\u102c\u1004\u103a\u101b\u100a\u103a\u1000\u102d\u102f \u1021\u1000\u1032\u1016\u103c\u1010\u103a\u101b\u1014\u103a\u104a \u1000\u103b\u103d\u1014\u103a\u102f\u1015\u103a\u1010\u102d\u102f\u1037\u101e\u100a\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103e\u1015\u103c\u102f\u101c\u102f\u1015\u103a\u101e\u1031\u102c \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u1001\u103b\u1000\u103a\u1019\u103b\u102c\u1038\u101e\u100a\u103a \u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u101c\u1031\u1037\u101c\u102c\u1011\u102c\u1038\u101e\u1031\u102c\u1012\u1031\u1010\u102c\u1014\u103e\u1004\u1037\u103a \u1019\u100a\u103a\u1019\u103b\u103e\u1000\u102d\u102f\u1000\u103a\u100a\u102e\u1000\u103c\u1031\u102c\u1004\u103a\u1038 \u1010\u102d\u102f\u1004\u103a\u1038\u1010\u102c\u101b\u1014\u103a \u101c\u102d\u102f\u1021\u1015\u103a\u1015\u102b\u101e\u100a\u103a\u104b \u1024\u1000\u1032\u1037\u101e\u102d\u102f\u1037\u101c\u102f\u1015\u103a\u1006\u1031\u102c\u1004\u103a\u101b\u1014\u103a\u1021\u1010\u103d\u1000\u103a \u1021\u101e\u102f\u1036\u1038\u1019\u103b\u102c\u1038\u101e\u1031\u102c\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f Leave-One-Out Cross-Validation (LOOCV) \u101f\u102f\u1001\u1031\u102b\u103a\u101e\u100a\u103a\u104a \u1021\u1031\u102c\u1000\u103a\u1015\u102b\u1014\u100a\u103a\u1038\u101c\u1019\u103a\u1038\u1000\u102d\u102f\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u101e\u100a\u103a- 1. \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u104f \u1010\u1005\u103a\u1005\u102d\u1010\u103a\u1010\u1005\u103a\u1015\u102d\u102f\u1004\u103a\u1038\u1021\u1016\u103c\u1005\u103a \u1019\u103e\u1010\u103a\u101e\u102c\u1038\u1019\u103e\u102f\u1021\u102c\u1038\u101c\u102f\u1036\u1038\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1012\u1031\u1010\u102c\u1021\u1005\u102f\u1036\u1000\u102d\u102f \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u101b\u1031\u1038\u1021\u1005\u102f\u1036\u1014\u103e\u1004\u1037\u103a \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f\u1010\u1005\u103a\u1001\u102f\u1021\u1016\u103c\u1005\u103a \u1001\u103d\u1032\u101c\u102d\u102f\u1000\u103a\u1015\u102b\u104b 2. \u101c\u1031\u1037\u1000\u103b\u1004\u1037\u103a\u1019\u103e\u102f\u1021\u1005\u102f\u1036\u1019\u103e\u1012\u1031\u1010\u102c\u1000\u102d\u102f\u101e\u102c\u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u104d \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1010\u1005\u103a\u1001\u102f\u1016\u1014\u103a\u1010\u102e\u1038\u1015\u102b\u104b 3. \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1019\u103e\u1016\u101a\u103a\u1011\u102f\u1010\u103a\u1011\u102c\u1038\u101e\u1031\u102c \u1005\u1031\u102c\u1004\u1037\u103a\u1000\u103c\u100a\u1037\u103a\u1019\u103e\u102f\u1010\u1014\u103a\u1016\u102d\u102f\u1038\u1000\u102d\u102f \u1001\u1014\u1037\u103a\u1019\u103e\u1014\u103a\u1038\u101b\u1014\u103a\u1014\u103e\u1004\u1037\u103a \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1005\u1010\u102f\u101b\u1014\u103a\u1038\u1021\u1019\u103e\u102c\u1038 (MSE) \u1000\u102d\u102f \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u101b\u1014\u103a \u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1000\u102d\u102f \u1021\u101e\u102f\u1036\u1038\u1015\u103c\u102f\u1015\u102b\u104b 4. \u1024\u101c\u102f\u1015\u103a\u1004\u1014\u103a\u1038\u1005\u1009\u103a n \u1021\u1000\u103c\u102d\u1019\u103a\u1019\u103b\u102c\u1038\u1000\u102d\u102f \u1015\u103c\u1014\u103a\u101c\u102f\u1015\u103a\u1015\u102b\u104b \u1005\u102c\u1019\u1031\u1038\u1015\u103d\u1032 MSE \u1000\u102d\u102f \u1005\u1019\u103a\u1038\u101e\u1015\u103a\u1019\u103e\u102f MSE \u1021\u102c\u1038\u101c\u102f\u1036\u1038\u104f \u1015\u103b\u1019\u103a\u1038\u1019\u103b\u103e\u1021\u1016\u103c\u1005\u103a \u1010\u103d\u1000\u103a\u1001\u103b\u1000\u103a\u1015\u102b\u104b \u1024\u101e\u1004\u103a\u1001\u1014\u103a\u1038\u1005\u102c\u101e\u100a\u103a Python \u1010\u103d\u1004\u103a\u1015\u1031\u1038\u1011\u102c\u1038\u101e\u1031\u102c\u1019\u1031\u102c\u103a\u1012\u101a\u103a\u1021\u1010\u103d\u1000\u103a […]<\/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":"\nPython \u1010\u103d\u1004\u103a \u1010\u1005\u103a\u1001\u102f\u1010\u100a\u103a\u1038-\u1021\u1011\u103d\u1000\u103a Cross-Validation (\u1025\u1015\u1019\u102c\u1019\u103b\u102c\u1038\u1014\u103e\u1004\u1037\u103a\u1021\u1010\u1030)<\/title>\n<meta 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