\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08\ubd80\ud130 \uace0\uae09 \uac1c\ub150, \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \uc778\uac04\uc758 \uac1c\uc785 \uc5c6\uc774 \uc124\uc815\ub41c \uaddc\uce59\uc5d0 \ub530\ub77c \uc790\ub3d9\uc73c\ub85c \ub9e4\ub9e4\ub97c \uc9c4\ud589\ud558\ub294 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc774\ub97c \uae30\ubc18\uc73c\ub85c \uc608\uce21 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8(Random Forest)\ub294 \uc5ec\ub7ec \uac1c\uc758 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc870\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \uc0dd\uc131\ud558\ub294 \uc559\uc0c1\ube14 \ud559\uc2b5 \uae30\ubc95\uc785\ub2c8\ub2e4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\ub294 \ub2e4\uc591\ud55c \uc758\uc0ac\uacb0\uc815 \ud2b8\ub9ac\ub97c \uc0dd\uc131\ud558\ub294\ub370, \uac01 \ud2b8\ub9ac\ub294 \ub370\uc774\ud130 \uc0d8\ud50c\uc758 \ub79c\ub364 \uc11c\ube0c\uc14b\uc5d0 \ub300\ud574 \ud559\uc2b5\ud569\ub2c8\ub2e4. \ucd5c\ucd08\uc758 \ub2e8\uacc4\ub85c, \uc8fc\uc2dd \ub370\uc774\ud130\uc14b\uc744 \uc900\ube44\ud574\uc57c \ud569\ub2c8\ub2e4. \uc8fc\uc2dd \uac00\uaca9, \uac70\ub798\ub7c9, \uae30\uc220\uc801 \uc9c0\ud45c \ub4f1 \ub2e4\uc591\ud55c \ud2b9\uc9d5\uc744 \uace0\ub824\ud574\uc57c \ud569\ub2c8\ub2e4.<\/p>\n \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud574 \ub370\uc774\ud130\uc14b\uc744 \ud6c8\ub828 \uc138\ud2b8\uc640 \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub85c \ub098\ub204\uc5b4\uc57c \ud569\ub2c8\ub2e4. \ubaa8\ub378 \uc870\uc815\uc740 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd\uc744 \ud1b5\ud574 \uc218\ud589\ub429\ub2c8\ub2e4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\uc758 \uc8fc\uc694 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \uadf8\ub9ac\ub4dc \uc11c\uce58 \ubc29\ubc95\uc744 \ud1b5\ud574 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\ud569\uc744 \uc2dc\ud5d8\ud558\uace0 \ucd5c\uc801\uc758 \uc870\ud569\uc744 \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uadf8\ub9ac\ub4dc \uc11c\uce58\uc758 \uc608\uc785\ub2c8\ub2e4.<\/p>\n \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \uc815\ud655\ub3c4, \uc815\ubc00\ub3c4, \uc7ac\ud604\uc728 \ubc0f F1 \uc810\uc218\uc640 \uac19\uc740 \uc5ec\ub7ec \uc9c0\ud45c\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ubaa8\ub378\uc744 \ud1b5\ud574 \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \ubd84\uc11d\ud569\ub2c8\ub2e4. Matplotlib\uc774\ub098 Seaborn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\ub97c \ub525\ub7ec\ub2dd \ubaa8\ub378\uacfc \uacb0\ud569\ud558\uc5ec \ub354 \ub098\uc740 \uc131\ub2a5\uc744 \ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 \ud2b9\uc9d5 \ucd94\ucd9c \ud6c4 \uc774\ub97c
\n \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\uc758 \ud559\uc2b5 \uacfc\uc815, \uadf8\ub9ac\uace0 \ubaa8\ub378 \ud29c\ub2dd \uc804\ub7b5\uc5d0 \ub300\ud574 \uc2ec\ub3c4 \uc788\uac8c \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.<\/p>\n<\/header>\n1. \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc758 \uae30\ucd08<\/h2>\n
\n \uc774 \uacfc\uc815\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc774 \uc810\uc810 \ub354 \ub9ce\uc774 \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ub370\uc774\ud130 \ubd84\uc11d\uacfc \uc608\uce21\uc758 \uc815\ud655\uc131\uc744 \ub192\uc774\uae30 \uc704\ud568\uc785\ub2c8\ub2e4.<\/p>\n1.1 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uc774\ud574<\/h3>\n
\n \ubc18\uba74, \ub525\ub7ec\ub2dd\uc740 \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc14b\uacfc \uc2e0\uacbd\ub9dd\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud560 \uc218 \uc788\ub294 \ubc29\ubc95\ub860\uc785\ub2c8\ub2e4.<\/p>\n<\/section>\n2. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\ub780?<\/h2>\n
\n \uc774 \uae30\ubc95\uc740 \uacfc\uc801\ud569 \ubb38\uc81c\ub97c \uc904\uc774\uace0, \uac15\ub825\ud55c \uc608\uce21 \uc815\ud655\ub3c4\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.<\/p>\n2.1 \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\uc758 \uc791\ub3d9 \uc6d0\ub9ac<\/h3>\n
\n \ud559\uc2b5\ub41c \ud2b8\ub9ac\ub4e4\uc758 \uc608\uce21 \uacb0\uacfc\ub97c \uc870\ud569\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n2.2 \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8\uc758 \uc7a5\uc810<\/h3>\n
\n
3. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubaa8\ub378 \ud559\uc2b5<\/h2>\n
3.1 \ub370\uc774\ud130 \uc900\ube44<\/h3>\n
3.2 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n
\n \uc77c\ubc18\uc801\uc73c\ub85c \ud6c8\ub828 \uc138\ud2b8\ub294 70%, \ud14c\uc2a4\ud2b8 \uc138\ud2b8\ub294 30%\ub85c \ub098\ub204\ub294 \uac83\uc774 \uc77c\ubc18\uc801\uc785\ub2c8\ub2e4.<\/p>\n3.3 \ubaa8\ub378 \uad6c\ucd95<\/h3>\n
\n
\n from sklearn.ensemble import RandomForestClassifier\n from sklearn.model_selection import train_test_split\n\n # \ub370\uc774\ud130 \uc900\ube44\n X = features # \ud2b9\uc9d5 \ub370\uc774\ud130\n y = target # \ubaa9\ud45c \ub370\uc774\ud130\n\n # \ub370\uc774\ud130 \ubd84\ud560\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n # \ubaa8\ub378 \ud6c8\ub828\n rf_model = RandomForestClassifier(n_estimators=100)\n rf_model.fit(X_train, y_train)\n <\/code>\n <\/pre>\n<\/section>\n4. \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubaa8\ub378 \uc870\uc815<\/h2>\n
4.1 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654<\/h3>\n
\n n_estimators, max_depth, min_samples_split, max_features \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n4.2 \uadf8\ub9ac\ub4dc \uc11c\uce58(Grid Search)<\/h3>\n
\n
\n from sklearn.model_selection import GridSearchCV\n\n param_grid = {\n 'n_estimators': [100, 200, 300],\n 'max_depth': [None, 10, 20, 30],\n 'min_samples_split': [2, 5, 10],\n }\n\n grid_search = GridSearchCV(estimator=rf_model, param_grid=param_grid, scoring='accuracy', cv=3)\n grid_search.fit(X_train, y_train)\n\n best_params = grid_search.best_params_\n <\/code>\n <\/pre>\n<\/section>\n5. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uc131\ub2a5 \ud5a5\uc0c1<\/h2>\n
5.1 \ubaa8\ub378 \ud3c9\uac00 \uc9c0\ud45c<\/h3>\n
5.2 \uc608\uce21 \ubc0f \uc2dc\uac01\ud654<\/h3>\n
\n
\n import matplotlib.pyplot as plt\n from sklearn.metrics import classification_report, confusion_matrix\n\n y_pred = rf_model.predict(X_test)\n\n print(confusion_matrix(y_test, y_pred))\n print(classification_report(y_test, y_pred))\n\n plt.figure(figsize=(10, 6))\n plt.plot(range(len(y_test)), y_test, label='True', color='blue')\n plt.plot(range(len(y_pred)), y_pred, label='Predicted', color='red')\n plt.legend()\n plt.show()\n <\/code>\n <\/pre>\n<\/section>\n6. \uace0\uae09 \uc8fc\uc81c: \ub525\ub7ec\ub2dd\uacfc\uc758 \ud1b5\ud569<\/h2>\n
\n \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8 \ubaa8\ub378\uc5d0 \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95\ub860\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<\/section>\n