\ub9ac\uc18c\uc2a4 \uc0ac\uc6a9 \ud6a8\uc728 \uadf9\ub300\ud654<\/li>\n<\/ul>\n2. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\ubaa8\ub378 \uc131\ub2a5\uc758 \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc785\ub2c8\ub2e4. \uc62c\ubc14\ub978 \uc804\ucc98\ub9ac\ub294 \ubaa8\ub378\uc774 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\ub3c4\ub85d \ub3c4\uc640\uc90d\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc758 \uc608\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
2.1 \ub370\uc774\ud130 \uc815\uc81c<\/h3>\n
\ub370\uc774\ud130 \uc815\uc81c\ub294 \ub370\uc774\ud130\uc14b\uc758 \uc7a1\uc74c\uc744 \uc81c\uac70\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378 \ud559\uc2b5\uc5d0 \ubc29\ud574\uac00 \ub418\ub294 \ub370\uc774\ud130\ub97c \uc0ac\uc804 \uc81c\uac70\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
import pandas as pd\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('dataset.csv')\n\n# \uacb0\uce21\uce58 \uc81c\uac70\ndata = data.dropna()\n\n# \uc911\ubcf5 \ub370\uc774\ud130 \uc81c\uac70\ndata = data.drop_duplicates()\n<\/code><\/pre>\n2.2 \ub370\uc774\ud130 \uc815\uaddc\ud654<\/h3>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \uc785\ub825 \ub370\uc774\ud130\uc758 \uc2a4\ucf00\uc77c\uc5d0 \ubbfc\uac10\ud558\uae30 \ub54c\ubb38\uc5d0, \ub370\uc774\ud130 \uc815\uaddc\ud654\ub294 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc815\uaddc\ud654 \ubc29\ubc95\uc740 \ub2e4\uc591\ud558\uc9c0\ub9cc, Min-Max \uc815\uaddc\ud654\uc640 Z-Score \uc815\uaddc\ud654\uac00 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n
from sklearn.preprocessing import MinMaxScaler\n\n# Min-Max \uc815\uaddc\ud654\nscaler = MinMaxScaler()\ndata[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])\n<\/code><\/pre>\n3. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815<\/h2>\n
\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ud559\uc2b5 \uacfc\uc815\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce58\ub294 \uc124\uc815 \uac12\uc785\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub85c\ub294 \ud559\uc2b5\ub960, \ubc30\uce58 \ud06c\uae30, \uc5d0\ud3ed \uc218 \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ucd5c\uc801\ud654\ub294 \ubaa8\ub378 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud558\uae30 \uc704\ud55c \uc911\uc694\ud55c \ub2e8\uacc4\uc785\ub2c8\ub2e4.<\/p>\n
3.1 \uadf8\ub9ac\ub4dc \uc11c\uce58(Grid Search)<\/h3>\n
\uadf8\ub9ac\ub4dc \uc11c\uce58\ub294 \ub2e4\uc591\ud55c \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\ud569\uc744 \uc2dc\ud5d8\ud558\uc5ec \ucd5c\uc801\uc758 \uc870\ud569\uc744 \ucc3e\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import SVC\n\n# \ud30c\ub77c\ubbf8\ud130 \uadf8\ub9ac\ub4dc \uc124\uc815\nparam_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}\n\n# \uadf8\ub9ac\ub4dc \uc11c\uce58 \uc2e4\ud589\ngrid_search = GridSearchCV(SVC(), param_grid, cv=5)\ngrid_search.fit(X_train, y_train)\n\n# \ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130 \ucd9c\ub825\nprint(\"\ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130:\", grid_search.best_params_)\n<\/code><\/pre>\n3.2 \ub79c\ub364 \uc11c\uce58(Random Search)<\/h3>\n
\ub79c\ub364 \uc11c\uce58\ub294 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uacf5\uac04\uc5d0\uc11c \ubb34\uc791\uc704\ub85c \uc0d8\ud50c\uc744 \uc120\ud0dd\ud558\uc5ec \ucd5c\uc801\uc758 \uc870\ud569\uc744 \ucc3e\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \uadf8\ub9ac\ub4dc \uc11c\uce58\ubcf4\ub2e4 \ub354 \ube60\ub974\uace0, \ub354 \ub098\uc740 \uacb0\uacfc\ub97c \uc5bb\uc744 \uc218 \uc788\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4.<\/p>\n
from sklearn.model_selection import RandomizedSearchCV\n\n# \ub79c\ub364 \uc11c\uce58 \uc2e4\ud589\nrandom_search = RandomizedSearchCV(SVC(), param_distributions=param_grid, n_iter=10, cv=5)\nrandom_search.fit(X_train, y_train)\n\n# \ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130 \ucd9c\ub825\nprint(\"\ucd5c\uc801\uc758 \ud30c\ub77c\ubbf8\ud130:\", random_search.best_params_)\n<\/code><\/pre>\n4. \ubaa8\ub378 \uad6c\uc870 \ucd5c\uc801\ud654<\/h2>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ucd5c\uc801\ud654\ud558\ub294 \ub610 \ub2e4\ub978 \ubc29\ubc95\uc740 \ubaa8\ub378 \uad6c\uc870\ub97c \uc870\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \ub808\uc774\uc5b4\uc758 \uc218, \ub274\ub7f0\uc758 \uc218, \ud65c\uc131\ud654 \ud568\uc218 \ub4f1\uc744 \uc870\uc815\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 \ub808\uc774\uc5b4 \ubc0f \ub274\ub7f0 \uc870\uc815<\/h3>\n
\ubaa8\ub378\uc758 \ub808\uc774\uc5b4 \uc218\uc640 \ub274\ub7f0 \uc218\ub97c \ubcc0\uacbd\ud558\uc5ec \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c \ud53c\ub4dc\ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd\uc758 \uc608\ub97c \ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\nclass SimpleNN(nn.Module):\n def __init__(self):\n super(SimpleNN, self).__init__()\n self.fc1 = nn.Linear(10, 20)\n self.fc2 = nn.Linear(20, 10)\n self.fc3 = nn.Linear(10, 1)\n \n def forward(self, x):\n x = torch.relu(self.fc1(x))\n x = torch.relu(self.fc2(x))\n return self.fc3(x)\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\nmodel = SimpleNN()\n<\/code><\/pre>\n4.2 \ud65c\uc131\ud654 \ud568\uc218 \uc120\ud0dd<\/h3>\n
\ud65c\uc131\ud654 \ud568\uc218\ub294 \uc2e0\uacbd\ub9dd\uc758 \ube44\uc120\ud615\uc131\uc744 \uacb0\uc815\ud558\uba70, \uc120\ud0dd\ub41c \ud65c\uc131\ud654 \ud568\uc218\uc5d0 \ub530\ub77c \ubaa8\ub378\uc758 \uc131\ub2a5\uc774 \ud06c\uac8c \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. ReLU, Sigmoid, Tanh \ub4f1 \ub2e4\uc591\ud55c \ud65c\uc131\ud654 \ud568\uc218\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4.<\/p>\n
def forward(self, x):\n x = torch.sigmoid(self.fc1(x)) # \ub2e4\ub978 \ud65c\uc131\ud654 \ud568\uc218 \uc0ac\uc6a9\n x = torch.relu(self.fc2(x))\n return self.fc3(x)\n<\/code><\/pre>\n5. \ud559\uc2b5 \uc18d\ub3c4 \uac1c\uc120<\/h2>\n
\ubaa8\ub378\uc758 \ud559\uc2b5 \uc18d\ub3c4\ub97c \uac1c\uc120\ud558\ub294 \uac83\uc740 \uaf2d \ud544\uc694\ud55c \uacfc\uc815\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \ub2e4\uc591\ud55c \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
5.1 \uc635\ud2f0\ub9c8\uc774\uc800 \uc120\ud0dd<\/h3>\n
\ub2e4\uc591\ud55c \uc635\ud2f0\ub9c8\uc774\uc800\uac00 \uc788\uc73c\uba70, \uac01 \uc635\ud2f0\ub9c8\uc774\uc800\ub294 \ud559\uc2b5 \uc18d\ub3c4\uc640 \uc131\ub2a5\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. Adam, SGD, RMSprop \ub4f1\uc774 \uc8fc\uc694 \uc635\ud2f0\ub9c8\uc774\uc800\uc785\ub2c8\ub2e4.<\/p>\n
optimizer = optim.Adam(model.parameters(), lr=0.001) # Adam \uc635\ud2f0\ub9c8\uc774\uc800 \uc0ac\uc6a9\n<\/code><\/pre>\n5.2 \uc870\uae30 \uc885\ub8cc(Early Stopping)<\/h3>\n
\uc870\uae30 \uc885\ub8cc\ub294 \uac80\uc99d \uc190\uc2e4\uc774 \ub354 \uc774\uc0c1 \uac10\uc18c\ud558\uc9c0 \uc54a\uc744 \ub54c \ud559\uc2b5\uc744 \uc911\ub2e8\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\uace0 \ud559\uc2b5 \uc2dc\uac04\uc744 \ub2e8\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
best_loss = float('inf')\npatience = 5 # \uc870\uae30 \uc885\ub8cc\ub97c \uc704\ud55c patience\ntrigger_times = 0\n\nfor epoch in range(epochs):\n # ... training code ...\n if validation_loss < best_loss:\n best_loss = validation_loss\n trigger_times = 0\n else:\n trigger_times += 1\n if trigger_times >= patience:\n print(\"\uc870\uae30 \uc885\ub8cc\")\n break\n<\/code><\/pre>\n6. \uacb0\ub860<\/h2>\n
\ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5 \ucd5c\uc801\ud654\ub97c \uc704\ud55c \ub2e4\uc591\ud55c \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc870\uc815, \ubaa8\ub378 \uad6c\uc870 \ucd5c\uc801\ud654 \ubc0f \ud559\uc2b5 \uc18d\ub3c4 \uac1c\uc120 \uae30\ubc95 \ub4f1\uc744 \ud1b5\ud574 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uadf9\ub300\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \uae30\ubc95\ub4e4\uc740 \uc5ec\ub7ec\ubd84\uc774 \ub525\ub7ec\ub2dd \uae30\uc220\uc744 \uc644\ubcbd\ud558\uac8c \ub2e4\ub8e8\uace0, \uc2e4\ubb34\uc5d0\uc11c \ub6f0\uc5b4\ub09c \uc131\uacfc\ub97c \uac70\ub450\ub294\ub370 \ub3c4\uc6c0\uc744 \uc904 \uac83\uc785\ub2c8\ub2e4.<\/p>\n