{"id":672,"date":"2020-12-09T23:02:34","date_gmt":"2020-12-09T14:02:34","guid":{"rendered":"http:\/\/cedartrees.co.kr\/?p=672"},"modified":"2021-01-09T19:18:36","modified_gmt":"2021-01-09T10:18:36","slug":"decesion-tree","status":"publish","type":"post","link":"http:\/\/blog.cedartrees.co.kr\/index.php\/2020\/12\/09\/decesion-tree\/","title":{"rendered":"\uacb0\uc815\ud2b8\ub9ac(Decision Tree)"},"content":{"rendered":"\n

\uacb0\uc815\ud2b8\ub9ac\ub294 \uacb0\ub860\uc5d0 \uc774\ub974\uae30 \uc704\ud574\uc11c “\uc608\/\uc544\ub2c8\uc624”\uc758 \ub2f5\ubcc0\uc744 \uc694\uad6c\ud558\ub294 \uc9c8\ubb38\uc744 \ubc18\ubcf5\ud558\uba74\uc11c \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \ub9c8\uce58 \uc2a4\ubb34\uace0\uac1c \uc9c8\ubb38\uacfc \ube44\uc2b7\ud558\uc8e0. \ucd5c\uc18c\ud55c\uc758 \uc801\uc740 \uc9c8\ubb38\uc73c\ub85c \uc5b4\ub5a4 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc9c8\ubb38\uc744 \uc798\ud574\uc57c \ud558\ub294\uac83\ucc98\ub7fc \uacb0\uc815\ud2b8\ub9ac\uc758 \uc911\uc694\ud55c \ubd80\ubd84\ub3c4 “\uc5b4\ub5bb\uac8c \uc9c8\ubb38\ud558\ub294\uac00?” \ud558\ub294 \uac83 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n

\"\"
https:\/\/kr.akinator.com\/<\/figcaption><\/figure>\n\n\n\n

\uc704\uc758 \ud504\ub85c\uadf8\ub7a8\uc740 “akinator”\ub77c\ub294 \uac8c\uc784\uc785\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\ub294 \uc544\ud0a4\ub124\uc774\ud130\uc758 \uc9c8\ubb38\uc5d0 \ub530\ub77c\uc11c \ub2f5\uc744 \uace0\ub974\uac8c \ub429\ub2c8\ub2e4. \uc774\ub7f0 \ubc29\ubc95\uc73c\ub85c \uba87\ucc28\ub840 \uc9c8\ubb38\uacfc \ub2f5\uc744 \uc8fc\uace0 \ubc1b\uc73c\uba74 \ub0b4\uac00 \uba38\ub9ac\uc18d\uc73c\ub85c \uadf8\ub824\ub0b8 \uc778\ubb3c\uc5d0 \ub300\ud574\uc11c \uc815\ud655\ud558\uac8c \uc608\uce21\ud569\ub2c8\ub2e4. \uc774\uac83\uc740 \uc774\ubbf8 \uc774 \ud504\ub85c\uadf8\ub7a8\uc744 \ud1b5\ud574 \uac8c\uc784\uc744 \uc9c4\ud589\ud588\ub358 \uc0ac\ub78c\ub4e4\uc758 \ub9ce\uc740 \ub370\uc774\ud130\uac00 \ucd95\uc801\ub418\uc5b4 \uc788\uae30 \ub54c\ubb38\uc5d0 \uac00\ub2a5\ud55c \uc77c\uc785\ub2c8\ub2e4. \uc81c\uac00 \ud14c\uc2a4\ud2b8 \ud588\ub358 \uc778\ubb3c\uc740 \ucd95\uad6c\uc120\uc218 \uc190\ud765\ubbfc\uc774 \uc774\uc5c8\ub294\ub370 \uc190\ud765\ubbfc\uc120\uc218\ub294 \uc774\ubbf8 222,308\ud68c \uc120\ud0dd\ub418\uc5b4 \uc84c\uae30 \ub54c\ubb38\uc5d0 \ub9ce\uc740 \ub370\uc774\ud130\uac00 \ucd95\uc801\ub418\uc5b4 \uc788\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \uba87\ubc88 \uc548\ub3fc\uc11c \uc544\ud0a4\ub124\uc774\ud130\uac00 \ub2f5\uc744 \ub9de\ucdc4\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n

\uacb0\uc815\ud2b8\ub9ac\uac00 \ub9c8\uce58 \uc774\uc640 \uac19\uc2b5\ub2c8\ub2e4. \uacb0\uc815\ud2b8\ub9ac\ub3c4 \ub370\uc774\ud130\uc14b\uc5d0\uc11c \uc9c8\ubb38\uc744 \ud1b5\ud574\uc11c \ubd84\ub958(classification) \uc791\uc5c5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \ub9e8 \ub9c8\uc9c0\ub9c9\uc5d0 \ub9c8\uc9c0\ub9c9 \ub2f5\uc774 \ub0a8\uc744 \ub54c\uae4c\uc9c0 \ub370\uc774\ud130\ub97c \ubd84\ub958\ud569\ub2c8\ub2e4. \uc774\ub807\uac8c \ub0a8\ub294 \ub9e8 \ub9c8\uc9c0\ub9c9 \ub178\ub4dc\ub97c \ub9ac\ud504(lefa) \ub178\ub4dc\ub77c\uace0 \ud569\ub2c8\ub2e4. \uadf8\ub9ac\uace0 \uac01 \ub178\ub4dc\ub4e4\uc740 \uc5e3\uc9c0(edge)\ub85c \uc5f0\uacb0\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n

\ubd84\ub958\uc5d0\ub3c4 \uc5ec\ub7ec \uc54c\uace0\ub9ac\uc998\ub4e4\uc774 \uc788\ub294\ub370 \uadf8\ub807\ub2e4\uba85 \uc5b8\uc81c \uacb0\uc815\ud2b8\ub9ac(Decision Tree)\ub97c \uc0ac\uc6a9\ud558\uba74 \uc88b\uc744\uae4c\uc694?
\uc608\ub97c \ub4e4\uc5b4\uc11c \uace0\uac1d\uc774 \uc740\ud589\uc5d0 \ub300\ucd9c\uc694\uccad\uc744 \ud588\uace0 \uc740\ud589\uc774 \uace0\uac1d\uc5d0 \ub300\ud55c \uc218\ub77d \uc694\uccad\uc744 \uac70\ubd80\ud588\uc744 \uacbd\uc6b0 \uace0\uac1d\uc5d0\uac8c \uc5b4\ub5a4 \uc774\uc720\ub85c \ub300\ucd9c\uc774 \ubd88\uac00\ud55c\uc9c0 \uc124\uba85\ud574\uc57c \ud55c\ub2e4\uba74 \ubc14\ub85c \uc774 \uc758\uc0ac\uacb0\uc815\ud2b8\ub9ac\uac00 \uc720\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998\uc774 \ub420 \uc218\uc788\uc2b5\ub2c8\ub2e4. <\/p>\n\n\n\n

\ud574\ub2f9 \uc54c\uace0\ub9ac\uc998\uc774 \uac00\uc7a5 \ub192\uc740 \uc131\ub2a5\uc744 \ubc1c\ud718\ud55c\ub2e4\ub294 \ubcf4\uc7a5\uc740 \uc5c6\uc9c0\ub9cc \uadf8\ub798\ub3c4 \uc5b4\ub5a4 \uacb0\uc815\uc5d0 \ub300\ud574\uc11c \uc124\uba85\uc774 \uac00\ub2a5\ud558\ub2e4\ub294 \uac83\uc740 \uc911\uc694\ud55c \uc774\uc720\uac00\ub429\ub2c8\ub2e4. \ubc14\ub85c \ud55c\ub208\uc5d0 \ubcf4\uace0 \uc774\ud574\ud560 \uc218 \uc788\ub2e4\ub294 \uc774\ub7ec\ud55c \uc815\uc810\uc774 \uc788\uae30 \ub54c\ubb38\uc5d0 \uc5b4\ub5a4 \uacb0\uc815\uc5d0 \ud569\ub9ac\uc801\uc778 \ud310\ub2e8 \uae30\uc900\uc73c\ub85c \uc81c\uc2dc\ud558\uae30 \uc704\ud574 \ud574\ub2f9 \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n

\uc758\uc0ac\uacb0\uc815\ub098\ubb34\uc740 \uc608\uce21 \ubcc0\uc218\ub97c \uae30\ubc18\uc73c\ub85c \uacb0\uacfc\ub97c \ubd84\ub958\ud558\uac70\ub098 \uc608\uce21\ud558\ub294\ub370 \uc774\ub7ec\ud55c \uc758\uc0ac\uacb0\uc815\uc758 \uaddc\uce59(Decision Rule)\uc744 \ub098\ud0c0\ub0b4\ub294 \uacfc\uc815\uc774 \ub9c8\uce58 \ub098\ubb34\uad6c\uc870\uc640 \uac19\ub2e4\uace0 \ud574\uc11c \ubd99\uc5ec\uc9c4 \uc774\ub984\uc785\ub2c8\ub2e4. \ud574\ub2f9 \uc54c\uace0\ub9ac\uc998\uc744 \ud65c\uc6a9\ud558\uba74 \ubd84\ub958(classification)\ub098 \uc608\uce21(prediction)\ub3c4 \uc218\ud589\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n

\uc544\ub798\uc758 \uc608\uc81c\ub294 \ubd93\uaf43 \ub370\uc774\ud130\ub97c Decision-Tree \uc54c\uace0\ub9ac\uc998\uc744 \ud1b5\ud574 \uad6c\uc131\ud55c \uacb0\uacfc\uc785\ub2c8\ub2e4.<\/p>\n\n\n\n

from sklearn.tree import DecisionTreeClassifier\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\n\n# DecicionTreeClassifier \uc0dd\uc131\ntree_iris = DecisionTreeClassifier()\n\n# \ubd93\uaf43 \ub370\uc774\ud130\ub97c \ub85c\ub529\ud558\uace0, \ud559\uc2b5\uacfc \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \uc138\ud2b8\ub85c \ubd84\ub9ac\niris_data = load_iris()\nX_train, X_test, y_train, y_test = train_test_split(iris_data.data, iris_data.target, test_size=0.2, random_state=11)\n\n# DecisionTreeClassifier \ud559\uc2b5\ntree_iris.fit(X_train, y_train)<\/pre>\n\n\n\n
from sklearn.tree import export_graphviz\nexport_graphviz(tree_iris, out_file='tree-iris.dot', class_names=['Setosa','Versicolour','Virginica'], feature_names=iris_data.feature_names, impurity=False, filled=True)<\/pre>\n\n\n\n
\ubd84\ub958\ub97c \uc218\ud589\ud558\uace0 \uadf8 \uacb0\uacfc\ub97c ‘tree_iris.dot’\uc774\ub77c\ub294 \ud30c\uc77c\ub85c \uae30\ub85d\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n
digraph Tree {\nnode [shape=box, style=\"filled\", color=\"black\"] ;\n0 [label=\"petal length (cm) <= 2.45\\nsamples = 120\\nvalue = [41, 40, 39]\\nclass = Setosa\", fillcolor=\"#fffdfd\"] ;\n1 [label=\"samples = 41\\nvalue = [41, 0, 0]\\nclass = Setosa\", fillcolor=\"#e58139\"] ;\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ...<\/pre>\n\n\n\n
\ud574\ub2f9 \ud30c\uc77c\uc744 \uc77d\uc5b4\ubcf4\uba74 \uc704\uc640 \uac19\uc774 \ucf54\ub4dc\uc758 \ud615\ud0dc\ub85c \ud45c\uc2dc\ub429\ub2c8\ub2e4. \uc774\ub807\uac8c  \ub9cc\ub4e4\uc5b4\uc9c4 \ucf54\ub4dc\ub97c \ubc14\ub85c \uc774\ud574\ud574\ub3c4 \ub418\uaca0\uc9c0\ub9cc \ubb34\ub9ac\uac00 \uc788\uc8e0. \ud30c\uc774\uc36c\uc758 graphviz\ub77c\ub294 \ud328\ud0a4\uc9c0\ub97c \uc774\uc6a9\ud558\uba74 \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub97c \ud6cc\ub96d\ud788 \uc2dc\uac01\ud654 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
# \uc544\ub798\uc640 \uac19\uc774 \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.\nconda install python-graphviz<\/pre>\n\n\n\n
\ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud558\uace0 tree-iris.dot \ud30c\uc77c\uc744 \uc77d\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
import graphviz\n\nwith open('.\/tree-iris.dot') as f:\n    dot_graph = f.read()\ndisplay(graphviz.Source(dot_graph))<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\uc704\uc758 \uadf8\ub9bc\uc744 \uc0b4\ud3b4\ubcf4\uba74 \uacb0\uc815\ud2b8\ub9ac\ub97c IF(petal length <= 2.45) THEN Setosa ELSE IF(petal width <= 1.55) and IF(petal length <= 5.25) THEN vesicolour \u2026\u00a0\uc640 \uac19\uc740 \ubc29\ubc95\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \uc5b4\ub5a4 \uacb0\uacfc\ub97c \uc5bb\uc5c8\uc744 \ub54c\uc5d0 \uaddc\uce59\uc744 \ud1b5\ud574\uc11c \ub370\uc774\ud130\ub97c \uc5bb\uc5c8\ub2e4\uace0 \uc774\uc57c\uae30 \ud560 \uc218 \uc788\ub294 \uac83\uc785\ub2c8\ub2e4.<\/p>\n\n\n\n
\uac00\uc7a5 \uc704\uc5d0 \uc788\ub294 \ub178\ub4dc\ub294 Root-Node \ub77c\uace0 \ud558\uace0 Parent-Child Node\uc758 \uc5f0\uacb0\uc774 \ub9c8\uce58 \ub098\ubb34\uc758 \uad6c\uc870\uc640 \uac19\ub2e4\uace0 \ud574\uc11c \uc758\uc0ac\uacb0\uc815\ub098\ubb34\ub77c\uace0\ub3c4 \ubd80\ub985\ub2c8\ub2e4. \uadf8\ub9ac\uace0 \ub9e8 \ub9c8\uc9c0\ub9c9\uc5d0 \uc788\ub294 \uac83\uc774 Teminal-Node\ub77c\uace0 \ubd80\ub985\ub2c8\ub2e4. \uac01 \ub178\ub4dc\uc758 \uc5f0\uacb0\uc120\uc744 \uc5e3\uc9c0(Edge) \ub77c\uace0 \ud558\uace0 Yes(True)\/No(False)\ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4.<\/p>\n\n\n\n
\ub8e8\ud2b8\ub178\ub4dc\ub294 \uac00\uc7a5 \uccab\ubc88\uc9f8 \uc9c8\ubb38\uc73c\ub85c \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774 \uc9c8\ubb38\uc740 \uac00\ub2a5\ud55c \ub9ce\uc740 \ub370\uc774\ud130\ub97c \ubd84\ub958 \ud560 \uc218 \uc788\ub294 \uc9c8\ubb38\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc704\uc758 \uacbd\uc6b0\ub294 Petal Length <= 2.45\ub97c \uae30\uc900\uc73c\ub85c \ucd1d 120\uac1c\uc758 \uc0d8\ud50c \ub370\uc774\ud130\ub97c 41:79\ub85c \ubd84\ub958\ud588\uc2b5\ub2c8\ub2e4. \uadf8\ub9ac\uace0 41\uc740 \uc804\ubd80 “Setosa”\ub77c\ub294 \uc885\ub958\ub77c\ub294 \uac83\uc744 \uc5bb\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8\ub9ac\uace0 \ub098\uba38\uc9c0 79\uac1c\uc758 \ub370\uc774\ud130\ub97c \ub2e4\uc2dc Petal Width <= 1.55\ub77c\ub294 \uc870\uac74\uc744 \ud1b5\ud574\uc11c 38:41\uc758 \ub370\uc774\ud130\ub85c \ubd84\ub958\ud574 \ub0c5\ub2c8\ub2e4.  \uc774\ub7f0 \uacfc\uc815\uc744 \ubc18\ubcf5\ud558\uba74\uc11c \ub370\uc774\ud130\ub97c \ub9c8\uc9c0\ub9c9\uac00\uc9c0 \ubd84\ub958\ud558\uac8c\ub429\ub2c8\ub2e4.<\/p>\n\n\n\n
\uc774 \uacb0\uc815\ud2b8\ub9ac\ub97c \uadf8\ub3d9\uc548 \uc0ac\uc6a9\ud588\ub358 fitness.csv\ub97c \ud1b5\ud574\uc11c \uc801\uc6a9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
dataset = pd.read_csv('.\/fitness.csv')\ndataset.loc[ dataset['age'] < 40, 'ACODE']= 1\ndataset.loc[ (dataset['age'] >= 40) & (dataset['age'] <50), 'ACODE']= 2\ndataset.loc[ (dataset['age'] >= 50) & (dataset['age'] <60), 'ACODE']= 3\n\nx_data = dataset[dataset.columns[1:-1]].values\ny_data = dataset[dataset.columns[-1]].values\n\nfit_tree = DecisionTreeClassifier()\nfit_tree.fit(x_data, y_data)\n\nfrom sklearn.tree import export_graphviz\nexport_graphviz(fit_tree, out_file='tree-fit.dot', class_names=['30','40','50'], feature_names=dataset.columns[1:-1], impurity=False, filled=True)\n\nwith open('.\/tree-fit.dot') as f:\n    dot_graph = f.read()\ndisplay(graphviz.Source(dot_graph))<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\uc704\uc5d0\uc11c \ubcf4\uc2dc\ub294\uac83\ucc98\ub7fc IF maxpulse <= 174 AND oxygen <= 46.723 THEN 50 \ub77c\ub294 \uc608\uce21\uacb0\uacfc\ub97c \uc5bb\uc5c8\uc2b5\ub2c8\ub2e4.  \uc774\ub7f0 \ubc29\ubc95\uc73c\ub85c \uc870\uac74\uc758 \ub9cc\uc871 \uc5ec\ubd80\ub97c \ub530\ub77c\uc11c \uc774\ub3d9\ud558\ub2e4\ubcf4\uba74 \uacb0\uad6d class \uac12\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n\n\n\n
\uc704 \uc608\uc81c\uc5d0\uc11c\ub294 \uc774\uc9c0\ubd84\ub9ac(Binary Split)\uc744 \uc0ac\uc6a9\ud588\uc9c0\ub9cc \uc54c\uace0\ub9ac\uc998\uc5d0 \ub530\ub77c\uc11c \ub2e4\uc9c0\ubd84\ub9ac(Multi Split)\ub3c4 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"
\uacb0\uc815\ud2b8\ub9ac\ub294 \uacb0\ub860\uc5d0 \uc774\ub974\uae30 \uc704\ud574\uc11c “\uc608\/\uc544\ub2c8\uc624”\uc758 \ub2f5\ubcc0\uc744 \uc694\uad6c\ud558\ub294 \uc9c8\ubb38\uc744 \ubc18\ubcf5\ud558\uba74\uc11c \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \ub9c8\uce58 \uc2a4\ubb34\uace0\uac1c \uc9c8\ubb38\uacfc \ube44\uc2b7\ud558\uc8e0. \ucd5c\uc18c\ud55c\uc758 \uc801\uc740 \uc9c8\ubb38\uc73c\ub85c \uc5b4\ub5a4 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc9c8\ubb38\uc744 \uc798\ud574\uc57c \ud558\ub294\uac83\ucc98\ub7fc \uacb0\uc815\ud2b8\ub9ac\uc758 \uc911\uc694\ud55c \ubd80\ubd84\ub3c4 “\uc5b4\ub5bb\uac8c \uc9c8\ubb38\ud558\ub294\uac00?” \ud558\ub294 \uac83 \uc911\uc694\ud569\ub2c8\ub2e4. \uc704\uc758 \ud504\ub85c\uadf8\ub7a8\uc740 “akinator”\ub77c\ub294 \uac8c\uc784\uc785\ub2c8\ub2e4. \uc0ac\uc6a9\uc790\ub294 \uc544\ud0a4\ub124\uc774\ud130\uc758 \uc9c8\ubb38\uc5d0 \ub530\ub77c\uc11c \ub2f5\uc744 \uace0\ub974\uac8c \ub429\ub2c8\ub2e4. \uc774\ub7f0 \ubc29\ubc95\uc73c\ub85c \uba87\ucc28\ub840 \uc9c8\ubb38\uacfc \ub2f5\uc744 \uc8fc\uace0 \ubc1b\uc73c\uba74 \ub0b4\uac00 \uba38\ub9ac\uc18d\uc73c\ub85c \uadf8\ub824\ub0b8 \uc778\ubb3c\uc5d0 … <\/p>\n
\ub354 \ubcf4\uae30 “\uacb0\uc815\ud2b8\ub9ac(Decision Tree)”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[17,70],"tags":[64,54,48,63,45,55],"_links":{"self":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/672"}],"collection":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/comments?post=672"}],"version-history":[{"count":8,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/672\/revisions"}],"predecessor-version":[{"id":685,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/posts\/672\/revisions\/685"}],"wp:attachment":[{"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/media?parent=672"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/categories?post=672"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/blog.cedartrees.co.kr\/index.php\/wp-json\/wp\/v2\/tags?post=672"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}