\ud000\ud2b8 \ud2b8\ub808\uc774\ub529\uc740 \ub370\uc774\ud130 \uae30\ubc18\uc758 \uacb0\uc815 \uacfc\uc815\uc744 \ud1b5\ud574 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uc218\uc775\uc744 \ucd94\uad6c\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc624\ub298\uc740 \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\uc778 k-\ucd5c\uadfc\uc811 \uc774\uc6c3(ker-nearest neighbors, KNN)\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uace0, \uc774\ub97c \ud1b5\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uac00\ub2a5\uc131\uc744 \ub17c\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
k-\ucd5c\uadfc\uc811 \uc774\uc6c3(KNN)\uc740 \ube44\ubaa8\uc218\uc801 \ubd84\ub958 \ubc0f \ud68c\uadc0 \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\ub85c, \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 ‘k’\uac1c\uc758 \uac00\uc7a5 \uac00\uae4c\uc6b4 \uc774\uc6c3\uc744 \uae30\uc900\uc73c\ub85c \ubd84\ub958\ub97c \uc218\ud589\ud569\ub2c8\ub2e4. KNN\uc758 \ud575\uc2ec \uac1c\ub150\uc740 ‘\uac70\ub9ac’\ub85c, \uc720\ud074\ub9ac\ub4dc \uac70\ub9ac, \ub9e8\ud574\ud2bc \uac70\ub9ac \ub4f1\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc774\uc6c3\uc744 \uacb0\uc815\ud558\uac8c \ub429\ub2c8\ub2e4. \uc774 \uc54c\uace0\ub9ac\uc998\uc740 \ub2e8\uc21c\ud558\uba74\uc11c\ub3c4 \uc9c1\uad00\uc801\uc774\uae30 \ub54c\ubb38\uc5d0 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ub9ce\uc774 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
KNN\uc758 \uae30\ubcf8 \uc791\ub3d9 \uc6d0\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\uc77c\ubc18\uc801\uc73c\ub85c KNN\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \uac70\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\uc758\ub429\ub2c8\ub2e4:<\/p>\n
\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac:<\/p>\n
\uc5ec\uae30\uc11c, D\ub294 \uac70\ub9ac, p\uc640 q\ub294 \ub450 \uac1c\uc758 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8, i\ub294 \uac01 \ud2b9\uc9d5\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/p>\n \uc774\uc81c KNN\uc744 \ud2b8\ub808\uc774\ub529\uc5d0 \uc5b4\ub5bb\uac8c \ud65c\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. KNN\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \uac00\uaca9 \uc608\uce21 \ub610\ub294 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 KNN\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc785\ub2c8\ub2e4.<\/p>\n \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \ub2e4\uc591\ud55c \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub294 \uc8fc\uac00, \uac70\ub798\ub7c9, \uae30\uc220\uc801 \uc9c0\ud45c \ub4f1\uc744 \ud3ec\ud568\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ub370\uc774\ud130\ub294 \ubcf4\ud1b5 CSV \ud30c\uc77c\uc774\ub098 \ub370\uc774\ud130\ubca0\uc774\uc2a4\uc5d0\uc11c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \uc218\uc9d1\ud55c \ub370\uc774\ud130\uc5d0\ub294 \uacb0\uce21\uac12, \uc774\uc0c1\uce58 \ub4f1\uc774 \ud3ec\ud568\ub420 \uc218 \uc788\uc73c\ubbc0\ub85c, \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc791\uc5c5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4:<\/p>\n \ub370\uc774\ud130\ub97c \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ubd84\ub9ac\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c 70%~80%\ub294 \ud6c8\ub828\uc5d0, \ub098\uba38\uc9c0\ub294 \ud14c\uc2a4\ud2b8\uc5d0 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/p>\n KNN \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0b5\ub2c8\ub2e4. K \uac12\uc740 \uc0ac\uc6a9\uc790\uac00 \uc124\uc815\ud574\uc57c \ud558\uba70, \uc5ec\ub7ec K \uac12\uc744 \uc2e4\ud5d8\ud574\ubcf4\uba70 \ucd5c\uc801\uc758 K \uac12\uc744 \ucc3e\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uacb0\uacfc\ub97c \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud63c\ub3d9 \ud589\ub82c, \uc815\ud655\ub3c4, F1 \uc810\uc218 \ub4f1\uc758 \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n KNN\uc758 \uc608\uce21 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uae30 \uc704\ud574 \uba87 \uac00\uc9c0 \ud301\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4:<\/p>\n K-\ucd5c\uadfc\uc811 \uc774\uc6c3\uc740 \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\ub85c, \uac04\ub2e8\ud558\uace0 \uc9c1\uad00\uc801\uc778 \ud2b9\uc131 \ub355\ubd84\uc5d0 \ud2b8\ub808\uc774\ub529\uc5d0 \uc801\uc6a9\ud558\uae30\uc5d0 \uc88b\uc740 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc640 \ubaa8\ub378 \ud3c9\uac00\uc5d0 \uc8fc\uc758\ub97c \uae30\uc6b8\uc778\ub2e4\uba74, KNN\uc744 \ud1b5\ud574 \ub9e4\uc6b0 \uc720\uc6a9\ud55c \uc608\uce21 \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc, \uace0\ucc28\uc6d0\uc758 \ub370\uc774\ud130\uc5d0\uc11c \ubc1c\uc0dd\ud560 \uc218 \uc788\ub294 \ubb38\uc81c\uc810\uacfc \uacc4\uc0b0 \ube44\uc6a9\uc744 \uace0\ub824\ud574\uc57c \ud55c\ub2e4\ub294 \uc810\uc744 \uc78a\uc9c0 \ub9c8\uc138\uc694. \ub2e4\uc74c \uae00\uc5d0\uc11c\ub294 KNN\uc758 \uace0\uae09 \ud65c\uc6a9\ubc95\uacfc \ub2e4\ub978 \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \ud3ec\uad04\ud560 \uac83\uc785\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4.<\/p>\n <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"D(p, q) = sqrt(\u2211(p_i - q_i)\u00b2)<\/code><\/p>\nKNN\uc758 \uc7a5\ub2e8\uc810<\/h2>\n
\uc7a5\uc810<\/h3>\n
\n
\ub2e8\uc810<\/h3>\n
\n
k-\ucd5c\uadfc\uc811 \uc774\uc6c3\uc744 \uc774\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529<\/h2>\n
1. \ub370\uc774\ud130 \uc218\uc9d1<\/h3>\n
2. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h3>\n
\n
3. \ub370\uc774\ud130 \ubd84\ud560<\/h3>\n
4. \ubaa8\ub378 \ud559\uc2b5<\/h3>\n
5. \uc608\uce21 \ubc0f \uacb0\uacfc \ud3c9\uac00<\/h3>\n
\uc608\uc81c \ucf54\ub4dc<\/h4>\n
\n
import pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('stock_data.csv')\n\n# \uc804\ucc98\ub9ac \uacfc\uc815 \uc608\uc2dc\ndata.fillna(method='ffill', inplace=True)\n\n# \ud2b9\uc9d5 \ubc0f \ud0c0\uac9f \ubcc0\uc218 \uc815\uc758\nX = data[['feature1', 'feature2', 'feature3']]\ny = data['target']\n\n# \ub370\uc774\ud130 \ubd84\ud560\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# KNN \ubaa8\ub378 \ud6c8\ub828\nmodel = KNeighborsClassifier(n_neighbors=5)\nmodel.fit(X_train, y_train)\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# \uacb0\uacfc \ud3c9\uac00\nprint(confusion_matrix(y_test, y_pred))\nprint(classification_report(y_test, y_pred))\n<\/code><\/pre>\n<\/div>\n\uc8fc\uc2dd \uac70\ub798 \uc608\uce21 \uc815\ud655\ub3c4 \ud5a5\uc0c1\uc744 \uc704\ud55c \ud301<\/h2>\n
\n
\uacb0\ub860<\/h2>\n