{"id":1176,"date":"2023-07-27T09:36:52","date_gmt":"2023-07-27T09:36:52","guid":{"rendered":"https:\/\/statorials.org\/ko\/python%e1%84%8b%e1%85%b4-k-%e1%84%8c%e1%85%a5%e1%86%b8%e1%84%80%e1%85%b5-%e1%84%80%e1%85%ad%e1%84%8e%e1%85%a1-%e1%84%80%e1%85%a5%e1%86%b7%e1%84%8c%e1%85%b3%e1%86%bc\/"},"modified":"2023-07-27T09:36:52","modified_gmt":"2023-07-27T09:36:52","slug":"python%e1%84%8b%e1%85%b4-k-%e1%84%8c%e1%85%a5%e1%86%b8%e1%84%80%e1%85%b5-%e1%84%80%e1%85%ad%e1%84%8e%e1%85%a1-%e1%84%80%e1%85%a5%e1%86%b7%e1%84%8c%e1%85%b3%e1%86%bc","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/python%e1%84%8b%e1%85%b4-k-%e1%84%8c%e1%85%a5%e1%86%b8%e1%84%80%e1%85%b5-%e1%84%80%e1%85%ad%e1%84%8e%e1%85%a1-%e1%84%80%e1%85%a5%e1%86%b7%e1%84%8c%e1%85%b3%e1%86%bc\/","title":{"rendered":"Python\uc758 k-fold \uad50\ucc28 \uac80\uc99d(\ub2e8\uacc4\ubcc4)"},"content":{"rendered":"

<\/p>\n

\n

\ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub300\ud55c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub824\uba74 \ubaa8\ub378\uc758 \uc608\uce21\uc774 \uad00\ucc30\ub41c \ub370\uc774\ud130\uc640 \uc5bc\ub9c8\ub098 \uc798 \uc77c\uce58\ud558\ub294\uc9c0 \uce21\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc774\ub97c \uc218\ud589\ud558\uae30 \uc704\ud574 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c \uc811\uadfc \ubc29\uc2dd\uc744 \uc0ac\uc6a9\ud558\ub294 k-\uacb9 \uad50\ucc28 \uac80\uc99d<\/a> \uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

1.<\/strong> \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub300\ub7b5 \ub3d9\uc77c\ud55c \ud06c\uae30\uc758 k\uac1c<\/em> \uadf8\ub8f9, \uc989 “\uc811\uae30”\ub85c \ubb34\uc791\uc704\ub85c \ub098\ub215\ub2c8\ub2e4.<\/span><\/p>\n

2.<\/strong> \uc811\ud78c \ubd80\ubd84 \uc911 \ud558\ub098\ub97c \uad6c\uc18d \uc138\ud2b8\ub85c \uc120\ud0dd\ud569\ub2c8\ub2e4. \ud15c\ud50c\ub9bf\uc744 \ub098\uba38\uc9c0 k-1 \uc811\uae30\ub85c \uc870\uc815\ud569\ub2c8\ub2e4. \uc778\uc7a5\ub41c \ud50c\ub77c\uc774\uc758 \uad00\ucc30\uc5d0 \ub300\ud55c MSE \ud14c\uc2a4\ud2b8\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/span><\/p>\n

3.<\/strong> \ub9e4\ubc88 \ub2e4\ub978 \uc138\ud2b8\ub97c \uc81c\uc678 \uc138\ud2b8\ub85c \uc0ac\uc6a9\ud558\uc5ec \uc774 \ud504\ub85c\uc138\uc2a4\ub97c k<\/em> \ubc88 \ubc18\ubcf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n

4.<\/strong> k\uac1c\uc758<\/em> \ud14c\uc2a4\ud2b8 MSE\uc758 \ud3c9\uade0\uc73c\ub85c \uc804\uccb4 \ud14c\uc2a4\ud2b8 MSE\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/span><\/p>\n

\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c \ud2b9\uc815 \ubaa8\ub378\uc5d0 \ub300\ud574 k-\uacb9 \uad50\ucc28 \uac80\uc99d\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub2e8\uacc4\ubcc4 \uc608\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/span><\/p>\n

1\ub2e8\uacc4: \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \ub85c\ub4dc<\/strong><\/span><\/h3>\n

\uba3c\uc800 \uc774 \uc608\uc81c\uc5d0 \ud544\uc694\ud55c \ud568\uc218\uc640 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4.<\/span><\/p>\n

 from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\nfrom<\/span> sklearn. model_selection<\/span> import<\/span> KFold\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
 2\ub2e8\uacc4: \ub370\uc774\ud130 \uc0dd\uc131<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c \ub450 \uac1c\uc758 \uc608\uce21 \ubcc0\uc218 x1<\/sub> \ubc0f x2<\/sub> \uc640 \ub2e8\uc77c \uc751\ub2f5 \ubcc0\uc218 y\ub97c \ud3ec\ud568\ud558\ub294 pandas DataFrame\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/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
 3\ub2e8\uacc4: K-\ud3f4\ub4dc \uad50\ucc28 \uac80\uc99d \uc218\ud589<\/strong><\/span><\/h3>\n
 \ub2e4\uc74c\uc73c\ub85c \ub2e4\uc911 \uc120\ud615 \ud68c\uadc0 \ubaa8\ub378\uc744<\/a> \ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub9de\ucd94\uace0 LOOCV\ub97c \uc218\ud589\ud558\uc5ec \ubaa8\ub378 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/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<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 10<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span>\n\n#build multiple linear regression model\n<\/span>model = LinearRegression()\n\n#use k-fold CV 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\n3.6141267491803646\n<\/strong><\/span><\/pre>\n
 \uadf8 \uacb0\uacfc, \ud3c9\uade0\uc808\ub300\uc624\ucc28(MAE)\uac00 3.614<\/strong> \uc784\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc989, \ubaa8\ub378 \uc608\uce21\uacfc \uc2e4\uc81c \uad00\ucc30\ub41c \ub370\uc774\ud130 \uac04\uc758 \ud3c9\uade0 \uc808\ub300 \uc624\ucc28\ub294 3.614\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc77c\ubc18\uc801\uc73c\ub85c MAE\uac00 \ub0ae\uc744\uc218\ub85d \ubaa8\ub378\uc774 \uc2e4\uc81c \uad00\uce21\uce58\ub97c \ub354 \uc798 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ubaa8\ub378 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ub610 \ub2e4\ub978 \uce21\uc815\ud56d\ubaa9\uc740 RMSE(\ud3c9\uade0 \uc81c\uacf1\uadfc \uc624\ucc28)\uc785\ub2c8\ub2e4. \ub2e4\uc74c \ucf54\ub4dc\ub294 LOOCV\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc774 \uce21\uc815\ud56d\ubaa9\uc744 \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/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<\/span> =<\/span> KFold<\/span> (<\/span> n_splits<\/span> =<\/span> 5<\/span> ,<\/span> random_state<\/span> =<\/span> 1<\/span> ,<\/span> shuffle<\/span> =<\/span> True<\/span> )<\/span> \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\n4.284373111711816<\/strong><\/span><\/pre>\n
 \uacb0\uacfc\uc5d0\uc11c RMSE(\uc81c\uacf1\ud3c9\uade0\uc81c\uacf1\uadfc \uc624\ucc28)\uac00 4.284<\/strong> \uc784\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 RMSE\uac00 \ub0ae\uc744\uc218\ub85d \ubaa8\ub378\uc774 \uc2e4\uc81c \uad00\uce21\uce58\ub97c \ub354 \uc798 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2e4\uc81c\ub85c \uc6b0\ub9ac\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \uc5ec\ub7ec \uac00\uc9c0 \ubaa8\ub378\uc744 \uc801\ud569\ud558\uace0 \uac01 \ubaa8\ub378\uc758 RMSE \ub610\ub294 MAE\ub97c \ube44\uad50\ud558\uc5ec \ud14c\uc2a4\ud2b8 \uc624\ub958\uc728\uc774 \uac00\uc7a5 \ub0ae\uc740 \ubaa8\ub378\uc744 \uacb0\uc815\ud558\ubbc0\ub85c \uc0ac\uc6a9\ud558\uae30\uc5d0 \uac00\uc7a5 \uc88b\uc740 \ubaa8\ub378\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \ub610\ud55c \uc774 \uc608\uc5d0\uc11c\ub294 k=5 \ud3f4\ub4dc\ub97c \uc0ac\uc6a9\ud558\uae30\ub85c \uc120\ud0dd\ud588\uc9c0\ub9cc \uc6d0\ud558\ub294 \ub9cc\ud07c\uc758 \ud3f4\ub4dc \uc218\ub97c \uc120\ud0dd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \uc2e4\uc81c\ub85c \uc6b0\ub9ac\ub294 \uc77c\ubc18\uc801\uc73c\ub85c 5~10\uacb9 \uc0ac\uc774\ub97c \uc120\ud0dd\ud569\ub2c8\ub2e4. \uc774\ub294 \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ud14c\uc2a4\ud2b8 \uc624\ub958\uc728\uc744 \uc0dd\uc131\ud558\ub294 \ucd5c\uc801\uc758 \ud50c\ub77c\uc774 \uc218\uc784\uc774 \uc785\uc99d\ub418\uc5c8\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/strong><\/span><\/h3>\n
 K-\ud3f4\ub4dc \uad50\ucc28 \uac80\uc99d \uc18c\uac1c<\/a>
 Python\uc758 \uc120\ud615 \ud68c\uadc0\uc5d0 \ub300\ud55c \uc644\uc804\ud55c \uac00\uc774\ub4dc<\/a>
 Python\uc758 Leave-One-Out \uad50\ucc28 \uac80\uc99d<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
\ub370\uc774\ud130 \uc138\ud2b8\uc5d0 \ub300\ud55c \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub824\uba74 \ubaa8\ub378\uc758 \uc608\uce21\uc774 \uad00\ucc30\ub41c \ub370\uc774\ud130\uc640 \uc5bc\ub9c8\ub098 \uc798 \uc77c\uce58\ud558\ub294\uc9c0 \uce21\uc815\ud574\uc57c \ud569\ub2c8\ub2e4. \uc774\ub97c \uc218\ud589\ud558\uae30 \uc704\ud574 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c \uc811\uadfc \ubc29\uc2dd\uc744 \uc0ac\uc6a9\ud558\ub294 k-\uacb9 \uad50\ucc28 \uac80\uc99d \uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4. 1. \ub370\uc774\ud130 \uc138\ud2b8\ub97c \ub300\ub7b5 \ub3d9\uc77c\ud55c \ud06c\uae30\uc758 k\uac1c \uadf8\ub8f9, \uc989 “\uc811\uae30”\ub85c \ubb34\uc791\uc704\ub85c \ub098\ub215\ub2c8\ub2e4. 2. \uc811\ud78c \ubd80\ubd84 \uc911 \ud558\ub098\ub97c \uad6c\uc18d \uc138\ud2b8\ub85c \uc120\ud0dd\ud569\ub2c8\ub2e4. \ud15c\ud50c\ub9bf\uc744 \ub098\uba38\uc9c0 k-1 \uc811\uae30\ub85c […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-1176","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"\nPython\uc758 K-\ud3f4\ub4dc \uad50\ucc28 \uac80\uc99d(\ub2e8\uacc4\ubcc4) - 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