{"id":28746,"date":"2024-10-27T16:14:52","date_gmt":"2024-10-27T16:14:52","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=28746"},"modified":"2024-11-26T06:58:11","modified_gmt":"2024-11-26T06:58:11","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-sklearn%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/28746\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, sklearn\uc744 \uc774\uc6a9\ud55c \ub77c\uc3d8 \ud68c\uadc0 \ubd84\uc11d"},"content":{"rendered":"

<\/p>\n

\n \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ud6a8\uc728\uc801\uc73c\ub85c \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9ac\uae30 \uc704\ud574 \ub9ce\uc740 \ud2b8\ub808\uc774\ub354\ub4e4\uc740
\n \uba38\uc2e0\ub7ec\ub2dd<\/strong>\uacfc \ub525\ub7ec\ub2dd<\/strong> \uae30\uc220\uc744 \ud65c\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\uc220\ub4e4\uc740
\n \ubc29\ub300\ud55c \uc591\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0, \uc2dc\uc7a5\uc758 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \ubcf4\ub2e4
\n \uc815\uad50\ud55c \uc608\uce21\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294
\n scikit-learn<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub77c\uc3d8 \ud68c\uadc0 \ubd84\uc11d\uc744 \ud1b5\ud574
\n \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n<\/p>\n

1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08<\/h2>\n

\n \uba38\uc2e0\ub7ec\ub2dd\uc740 \ucef4\ud4e8\ud130\uac00 \uba85\uc2dc\uc801\uc73c\ub85c \ud504\ub85c\uadf8\ub7a8\ub418\uc9c0 \uc54a\uace0\ub3c4 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \ud559\uc2b5\ud558\ub3c4\ub85d \ud558\ub294
\n \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uba38\uc2e0\ub7ec\ub2dd \uc811\uadfc \ubc29\uc2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744
\n \ucc3e\uc544\ub0b4\uace0, \uc774\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubbf8\ub798\uc758 \uac00\uaca9 \ubcc0\ub3d9\uc744 \uc608\uce21\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub461\ub2c8\ub2e4.\n<\/p>\n

\n \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c\uc11c, \ud2b9\ud788 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \uad6c\uc870\ub97c \ucc98\ub9ac\ud558\ub294 \ub370 \uac15\uc810\uc744
\n \ubcf4\uc785\ub2c8\ub2e4. \uc2e0\uacbd\ub9dd \uad6c\uc870\ub97c \uae30\ubc18\uc73c\ub85c \ud558\uc5ec \ub9e4\uc6b0 \ubc29\ub300\ud55c \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c
\n \ub192\uc740 \ucc28\uc6d0\uc758 \ud2b9\uc9d5\uc744 \ucd94\ucd9c\ud558\uace0 \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n

2. \ub77c\uc3d8 \ud68c\uadc0(Lasso Regression)\ub780?<\/h2>\n

\n \ub77c\uc3d8 \ud68c\uadc0\ub294 \uc120\ud615 \ud68c\uadc0\uc758 \ubcc0\ud615\uc73c\ub85c, \ud2b9\uc131 \uc120\ud0dd(feature selection)\uacfc \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc758
\n \ucc98\ub9ac\ub97c \uc704\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 L1 \uc815\uaddc\ud654<\/strong>\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud68c\uadc0\uc5d0\uc11c
\n \uc0ac\uc6a9\ub418\ub294 \ubcc0\uc218\uc758 \uac1c\uc218\ub97c \uc904\uc774\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4. L1 \uc815\uaddc\ud654\ub294 \uc77c\ubd80 \ud68c\uadc0 \uacc4\uc218\ub97c
\n 0\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \ubd88\ud544\uc694\ud55c \ud2b9\uc131\uc744 \uc81c\uac70\ud558\ub294 \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4.\n<\/p>\n

\n \ub77c\uc3d8 \ud68c\uadc0\uc758 \uc8fc\uc694 \uc7a5\uc810\uc740 \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc5d0\uc11c\ub3c4 \uac04\ub2e8\ud558\uace0 \ud574\uc11d \uac00\ub2a5\ud55c \ubaa8\ub378\uc744
\n \uc0dd\uc131\ud560 \uc218 \uc788\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4. \ub610\ud55c, \uc77c\ubc18\ud654\ub41c \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\ub294 \ub370 \uc720\ub9ac\ud569\ub2c8\ub2e4.\n<\/p>\n

3. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n

\n \ubcf8 \uc608\uc81c\uc5d0\uc11c\ub294 \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub77c\uc3d8 \ud68c\uadc0 \ubaa8\ub378\uc744 \ud559\uc2b5\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n Yahoo Finance \ub610\ub294 Quandl \uac19\uc740 \uc18c\uc2a4\uc5d0\uc11c \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uac00\uc838\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc5ec\uae30\uc11c\ub294 pandas\ub97c \uc774\uc6a9\ud574 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.\n<\/p>\n

\nimport pandas as pd\n\n# \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud569\ub2c8\ub2e4.\ndata = pd.read_csv('stock_data.csv')\n\n# \ub370\uc774\ud130\uc758 \uccab 5\uc904\uc744 \ucd9c\ub825\ud569\ub2c8\ub2e4.\nprint(data.head())\n<\/code><\/pre>\n
4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\n    \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub294 \uba38\uc2e0\ub7ec\ub2dd\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uacb0\uce21\uce58 \ucc98\ub9ac,
\n    \uc774\uc0c1\uce58 \uc81c\uac70, \ud2b9\uc131 \uc2a4\ucf00\uc77c\ub9c1 \ub4f1\uc758 \uc791\uc5c5\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \ub610\ud55c,
\n    \ub77c\uc3d8 \ud68c\uadc0\ub294 \uc911\uc694\ud558\uc9c0 \uc54a\uc740 \ubcc0\uc218\ub97c \uc790\ub3d9\uc73c\ub85c \uc81c\uac70\ud558\uc9c0\ub9cc,
\n    \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \ub192\uc774\ub294 \uac83\ub3c4 \ud544\uc218\uc801\uc785\ub2c8\ub2e4.\n<\/p>\n
\n# \uacb0\uce21\uce58 \ucc98\ub9ac\ndata.fillna(method='ffill', inplace=True)\n\n# \ud2b9\uc131 \ubc0f \ud0c0\uac9f \ubcc0\uc218 \uc124\uc815\nX = data[['feature1', 'feature2', 'feature3']]\ny = data['target']\n<\/code><\/pre>\n
5. \ub370\uc774\ud130 \ubd84\ub9ac<\/h2>\n
\n    \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \ub098\ub204\ub294 \uac83\uc740 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744
\n    \ud3c9\uac00\ud558\ub294 \ub370 \uc911\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c 70-80%\uc758 \ub370\uc774\ud130\ub97c
\n    \ud6c8\ub828\uc5d0 \uc0ac\uc6a9\ud558\uace0, \ub098\uba38\uc9c0\ub97c \ud14c\uc2a4\ud2b8\uc5d0 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n<\/p>\n
\nfrom sklearn.model_selection import train_test_split\n\n# \ub370\uc774\ud130 \ubd84\ub9ac\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n<\/code><\/pre>\n
6. \ub77c\uc3d8 \ud68c\uadc0 \ubaa8\ub378 \uc0dd\uc131<\/h2>\n
\n    \uc774\uc81c scikit-learn<\/code>\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub77c\uc3d8 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0dd\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4.
\n    \ub77c\uc3d8 \ud68c\uadc0\ub294 Lasso<\/code> \ud074\ub798\uc2a4\ub97c \ud1b5\ud574 \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\nfrom sklearn.linear_model import Lasso\n\n# \ub77c\uc3d8 \ud68c\uadc0 \ubaa8\ub378 \ucd08\uae30\ud654\nlasso_model = Lasso(alpha=0.1)\n\n# \ubaa8\ub378 \ud559\uc2b5\nlasso_model.fit(X_train, y_train)\n<\/code><\/pre>\n
7. \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n
\n    \ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.
\n    mean_squared_error<\/code> \ud568\uc218\ub97c \ud1b5\ud574 \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28(MSE)\ub97c \uacc4\uc0b0\ud558\uace0,
\n    R^2 \uc2a4\ucf54\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \uc124\uba85\ub825\uc744 \ud3c9\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\nfrom sklearn.metrics import mean_squared_error, r2_score\n\n# \uc608\uce21\ny_pred = lasso_model.predict(X_test)\n\n# MSE \ubc0f R^2 \uc2a4\ucf54\uc5b4 \uacc4\uc0b0\nmse = mean_squared_error(y_test, y_pred)\nr2 = r2_score(y_test, y_pred)\n\nprint('MSE:', mse)\nprint('R^2 Score:', r2)\n<\/code><\/pre>\n
8. \ubaa8\ub378 \ud574\uc11d<\/h2>\n
\n    \ub77c\uc3d8 \ud68c\uadc0\ub294 \ud68c\uadc0 \uacc4\uc218\ub97c \ud1b5\ud574 \uac01 \ud2b9\uc131\uc774 \ud0c0\uac9f \ubcc0\uc218\uc5d0 \ubbf8\uce58\ub294 \uc601\ud5a5\uc744
\n    \ud574\uc11d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 0\uc774 \uc544\ub2cc \uacc4\uc218\ub97c \uac00\uc9c4 \ud2b9\uc131\uc740 \uc720\uc758\ubbf8\ud558\uac8c \ubaa8\ub378\uc5d0
\n    \uae30\uc5ec\ud558\uace0 \uc788\uc74c\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n<\/p>\n
\n# \ud68c\uadc0 \uacc4\uc218 \ucd9c\ub825\ncoefficients = pd.DataFrame(lasso_model.coef_, X.columns, columns=['Coefficient'])\nprint(coefficients)\n<\/code><\/pre>\n
9. \ucd94\uac00\uc801\uc778 \ucd5c\uc801\ud654<\/h2>\n
\n    \ub77c\uc3d8 \ud68c\uadc0\ub294 alpha \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\uc5d0 \ub530\ub77c \ubaa8\ub378\uc758 \ubcf5\uc7a1\uc131\uc774 \uacb0\uc815\ub429\ub2c8\ub2e4.
\n    \uad50\ucc28 \uac80\uc99d\uc744 \ud1b5\ud574 \ucd5c\uc801\uc758 alpha \uac12\uc744 \ucc3e\uace0 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574
\n    \ub17c\uc758\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\nfrom sklearn.model_selection import GridSearchCV\n\n# \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uadf8\ub9ac\ub4dc \uc124\uc815\nparam_grid = {'alpha': [0.001, 0.01, 0.1, 1, 10]}\n\n# \uadf8\ub9ac\ub4dc \uc11c\uce58 \ucd08\uae30\ud654\ngrid = GridSearchCV(Lasso(), param_grid, cv=5)\n\n# \ubaa8\ub378 \ud6c8\ub828\ngrid.fit(X_train, y_train)\n\nprint('Best alpha:', grid.best_params_)\n<\/code><\/pre>\n
10. \uacb0\ub860<\/h2>\n
\n    \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c\uc758
\n    \ub77c\uc3d8 \ud68c\uadc0 \ubd84\uc11d \uae30\ubc95\uc744 \ub2e4\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uc774 \ub808\uc2a8\uc744 \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc740
\n    \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc2dd \uac00\uaca9\uc744 \uc608\uce21\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6b0\uace0,
\n    \uc2e4\uc804\uc5d0\uc11c\uc758 \ub370\uc774\ud130 \uc804\ucc98\ub9ac, \ubaa8\ub378 \uad6c\ucd95, \ud3c9\uac00 \uacfc\uc815\uc744 \uc774\ud574\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4.
\n    \uc55e\uc73c\ub85c\ub3c4 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \ub354\uc6b1 \ubc1c\uc804\ub41c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744
\n    \uac1c\ubc1c\ud558\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n<\/p>\n
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\uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \ud6a8\uc728\uc801\uc73c\ub85c \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9ac\uae30 \uc704\ud574 \ub9ce\uc740 \ud2b8\ub808\uc774\ub354\ub4e4\uc740 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\uc220\ub4e4\uc740 \ubc29\ub300\ud55c \uc591\uc758 \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud558\uace0, \uc2dc\uc7a5\uc758 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \ubcf4\ub2e4 \uc815\uad50\ud55c \uc608\uce21\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 scikit-learn \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub77c\uc3d8 \ud68c\uadc0 \ubd84\uc11d\uc744 \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc744 \uc218\ud589\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uba38\uc2e0\ub7ec\ub2dd\uc740 \ucef4\ud4e8\ud130\uac00 \uba85\uc2dc\uc801\uc73c\ub85c \ud504\ub85c\uadf8\ub7a8\ub418\uc9c0 \uc54a\uace0\ub3c4 \ub370\uc774\ud130\ub97c \ud1b5\ud574 … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, sklearn\uc744 \uc774\uc6a9\ud55c \ub77c\uc3d8 \ud68c\uadc0 \ubd84\uc11d”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-28746","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, sklearn\uc744 \uc774\uc6a9\ud55c \ub77c\uc3d8 \ud68c\uadc0 \ubd84\uc11d - 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