{"id":29998,"date":"2024-10-28T03:19:05","date_gmt":"2024-10-28T03:19:05","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29998"},"modified":"2024-11-26T06:50:28","modified_gmt":"2024-11-26T06:50:28","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%ea%b2%b0%ec%a0%95-%ed%8a%b8%eb%a6%ac","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29998\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uacb0\uc815 \ud2b8\ub9ac"},"content":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc774 \ub525\ub7ec\ub2dd\uc774\ub77c \ubd88\ub9ac\uae30 \uc804\uc5d0, \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ucd08 \uc911 \ud558\ub098\ub294 &#8220;\uacb0\uc815 \ud2b8\ub9ac(Decision Tree)&#8221; \ubaa8\ub378\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \uacb0\uc815 \ud2b8\ub9ac\ub294 \uc124\uba85\uc774 \uc9c1\uad00\uc801\uc774\uace0 \uad6c\ud604\uc774 \uc26c\uc6b4 \ud3b8\uc774\ub77c \uc785\ubb38\uc790\uc5d0\uac8c \uc801\ud569\ud55c \ud559\uc2b5 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uacb0\uc815 \ud2b8\ub9ac\uac00 \ubb34\uc5c7\uc778\uc9c0, \uadf8\ub9ac\uace0 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc778 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud574 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc5b4\ub5bb\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294\uc9c0 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub860\uc801 \ubc30\uacbd\ubd80\ud130 \uc2dc\uc791\ud574 \ud30c\uc774\ud1a0\uce58\ub85c\uc758 \uad6c\ud604\uae4c\uc9c0, \ub2e4\uc591\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \uc774\ud574\ud558\uae30 \uc27d\uac8c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>1. \uacb0\uc815 \ud2b8\ub9ac\ub780?<\/h2>\n<p>\uacb0\uc815 \ud2b8\ub9ac\ub294 \uc774\ub984 \uadf8\ub300\ub85c, \ub098\ubb34(tree) \ubaa8\uc591\uc758 \uad6c\uc870\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \ubd84\ub958\ud558\uac70\ub098 \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc785\ub2c8\ub2e4. \ud2b8\ub9ac\ub294 &#8220;\ubfcc\ub9ac \ub178\ub4dc(root node)&#8221;\uc5d0\uc11c \uc2dc\uc791\ud574 \uc5ec\ub7ec \uac1c\uc758 &#8220;\uac00\uc9c0(branch)&#8221;\uc640 &#8220;\ub178\ub4dc(node)&#8221;\ub85c \ub098\ub269\ub2c8\ub2e4. \uac01 \ub178\ub4dc\ub294 \ub370\uc774\ud130\uc758 \ud2b9\uc815 \ud2b9\uc131(feature)\uc5d0 \ub300\ud55c \uc9c8\ubb38\uc744 \ub098\ud0c0\ub0b4\uba70, \uc9c8\ubb38\uc758 \ub300\ub2f5\uc5d0 \ub530\ub77c \ub370\uc774\ud130\ub97c \ub2e4\uc74c \uac00\uc9c0\ub85c \ubcf4\ub0c5\ub2c8\ub2e4. \uacb0\uad6d \ub9ac\ud504 \ub178\ub4dc(leaf node)\uc5d0 \ub3c4\ub2ec\ud558\uba74 \ub370\uc774\ud130\uc758 \ubd84\ub958 \uacb0\uacfc\ub098 \uc608\uce21 \uac12\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<p>\uacb0\uc815 \ud2b8\ub9ac\ub294 \uadf8 \ub2e8\uc21c\ud568 \ub355\ubd84\uc5d0 \uc124\uba85\ub825\uc774 \ub6f0\uc5b4\ub098\uba70, \ubaa8\ub378\uc758 \uac01 \ub2e8\uacc4\uc5d0\uc11c \uc5b4\ub5a4 \uacb0\uc815\uc744 \ub0b4\ub838\ub294\uc9c0\ub97c \uba85\ud655\ud558\uac8c \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub54c\ubb38\uc5d0 \uacb0\uc815 \ud2b8\ub9ac\ub294 \uc758\ub8cc \uc9c4\ub2e8\uc774\ub098 \uae08\uc735 \ubd84\uc11d \ub4f1, \uc758\uc0ac \uacb0\uc815 \uacfc\uc815\uc758 \uc124\uba85\uc774 \uc911\uc694\ud55c \ubb38\uc81c\ub4e4\uc5d0\uc11c \uc790\uc8fc \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/p>\n<h3>\uc608\uc81c: \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc774\uc6a9\ud55c \uac04\ub2e8\ud55c \ubd84\ub958 \ubb38\uc81c<\/h3>\n<p>\uc608\ub97c \ub4e4\uc5b4, \uc6b0\ub9ac\ub294 \ud559\uc0dd\ub4e4\uc774 \uc5b4\ub5a4 \uacfc\ubaa9\uc744 \uc88b\uc544\ud560\uc9c0\ub97c \uc608\uce21\ud558\uace0 \uc2f6\ub2e4\uace0 \uac00\uc815\ud574\ubd05\uc2dc\ub2e4. \ud2b8\ub9ac\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc9c8\ubb38\uc744 \ud1b5\ud574 \ud559\uc0dd\ub4e4\uc744 \ubd84\ub958\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>&#8220;\ud559\uc0dd\uc774 \uc218\ud559\uc744 \uc88b\uc544\ud558\ub098\uc694?&#8221;\n<ul>\n<li>\uc608: \uacfc\ud559 \uacfc\ubaa9\uc73c\ub85c \uc774\ub3d9<\/li>\n<li>\uc544\ub2c8\uc694: \uc778\ubb38\ud559 \uacfc\ubaa9\uc73c\ub85c \uc774\ub3d9<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\uc774\ub807\uac8c \uac01 \uc9c8\ubb38\uc744 \uac70\uccd0 \ub098\ubb34\uc758 \ub05d\uc5d0 \ub3c4\ub2ec\ud558\uba74, \uc6b0\ub9ac\ub294 \ud559\uc0dd\uc774 \uc5b4\ub5a4 \uacfc\ubaa9\uc744 \uc120\ud638\ud558\ub294\uc9c0\ub97c \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>2. \uacb0\uc815 \ud2b8\ub9ac\uc758 \uc7a5\uc810\uacfc \ub2e8\uc810<\/h2>\n<p>\uacb0\uc815 \ud2b8\ub9ac\ub294 \uba87 \uac00\uc9c0 \uc7a5\uc810\uacfc \ub2e8\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\uc7a5\uc810:<\/h3>\n<ul>\n<li><strong>\uc9c1\uad00\uc801<\/strong>: \uacb0\uc815 \ud2b8\ub9ac\ub294 \uc2dc\uac01\uc801\uc73c\ub85c \ud45c\ud604\ud560 \uc218 \uc788\uc5b4 \uc774\ud574\uac00 \uc27d\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uc124\uba85\ub825<\/strong>: \uac01 \uacb0\uc815\uc774 \uba85\ud655\ud558\uae30 \ub54c\ubb38\uc5d0 \uacb0\uacfc\ub97c \uc124\uba85\ud558\uae30 \uc6a9\uc774\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ube44\uc815\uaddc\ud654 \ub370\uc774\ud130 \ucc98\ub9ac<\/strong>: \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uac00 \ube44\uad50\uc801 \ub35c \ud544\uc694\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>\ub2e8\uc810:<\/h3>\n<ul>\n<li><strong>\uacfc\uc801\ud569(overfitting)<\/strong>: \uacb0\uc815 \ud2b8\ub9ac\ub294 \uae4a\uc774\uac00 \uae4a\uc5b4\uc9c8\uc218\ub85d \ud6c8\ub828 \ub370\uc774\ud130\uc5d0 \uacfc\uc801\ud569\ub418\uae30 \uc26c\uc6cc, \uc77c\ubc18\ud654 \ub2a5\ub825\uc774 \ub5a8\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ubcf5\uc7a1\ud55c \uacb0\uc815 \uacbd\uacc4<\/strong>: \uace0\ucc28\uc6d0\uc758 \ub370\uc774\ud130\uc5d0\uc11c\ub294 \uacb0\uc815 \ud2b8\ub9ac\uc758 \uacbd\uacc4\uac00 \ub108\ubb34 \ubcf5\uc7a1\ud574\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n<p>\uc774\ub7ec\ud55c \uc774\uc720\ub85c \uacb0\uc815 \ud2b8\ub9ac\ub294 \ub2e8\uc77c \ubaa8\ub378\ub85c\ub294 \ud55c\uacc4\uac00 \uc788\uc744 \uc218 \uc788\uc9c0\ub9cc, \uc559\uc0c1\ube14 \ud559\uc2b5(\uc608: \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8)\uacfc \uac19\uc740 \uae30\ubc95\uacfc \uacb0\ud569\ud558\uba74 \ub9e4\uc6b0 \uac15\ub825\ud55c \ubaa8\ub378\uc774 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>3. \ud30c\uc774\ud1a0\uce58\ub85c \uacb0\uc815 \ud2b8\ub9ac \uad6c\ud604\ud558\uae30<\/h2>\n<p>\ud30c\uc774\ud1a0\uce58\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uac1c\ubc1c\ud558\uae30 \uc704\ud55c \ub9e4\uc6b0 \uac15\ub825\ud55c \ud504\ub808\uc784\uc6cc\ud06c\uc774\uc9c0\ub9cc, \uacb0\uc815 \ud2b8\ub9ac \uac19\uc740 \uace0\uc804\uc801\uc778 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\ub3c4 \ud30c\uc774\ud1a0\uce58\ub97c \uc774\uc6a9\ud574 \ud559\uc2b5\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\ub9cc, \ud30c\uc774\ud1a0\uce58\uc5d0\ub294 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc9c1\uc811\uc801\uc73c\ub85c \uad6c\ud604\ud558\ub294 \uae30\ub2a5\uc774 \uc5c6\uc73c\ubbc0\ub85c, \uc774\ub97c \uc704\ud574 \ub2e4\ub978 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640\uc758 \uacb0\ud569\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c\ub294 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc704\ud55c <code>scikit-learn<\/code>\uc744 \uc0ac\uc6a9\ud558\uace0, \ud30c\uc774\ud1a0\uce58\uc640 \ud568\uaed8 \uc0ac\uc6a9\ud558\uc5ec \ubcf4\ub2e4 \ubcf5\uc7a1\ud55c \ubaa8\ub378\ub85c \ud655\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\uc608\uc81c: XOR \ubb38\uc81c \ud574\uacb0\ud558\uae30<\/h3>\n<pre><code>import numpy as np\nfrom sklearn.tree import DecisionTreeClassifier\nimport torch\nimport matplotlib.pyplot as plt\n\n# \ub370\uc774\ud130 \uc0dd\uc131\nX = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])\ny = np.array([0, 1, 1, 0])\n\n# \uacb0\uc815 \ud2b8\ub9ac \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ud559\uc2b5\nmodel = DecisionTreeClassifier()\nmodel.fit(X, y)\n\n# \uc608\uce21\npredictions = model.predict(X)\nprint(\"Predictions:\", predictions)\n\n# \ud30c\uc774\ud1a0\uce58 \ud150\uc11c\ub85c \ubcc0\ud658\ntensor_X = torch.tensor(X, dtype=torch.float32)\ntensor_predictions = torch.tensor(predictions, dtype=torch.float32)\nprint(\"Tensor Predictions:\", tensor_predictions)\n\n# \uc2dc\uac01\ud654\nplt.figure(figsize=(8, 6))\nfor i, (x, label) in enumerate(zip(X, y)):\n    plt.scatter(x[0], x[1], c='red' if label == 0 else 'blue', label=f'Class {label}' if i &lt; 2 else \"\")\n\nplt.xlabel('Feature 1')\nplt.ylabel('Feature 2')\nplt.title('XOR Problem Visualization')\nplt.legend()\nplt.grid(True)\nplt.show()<\/code><\/pre>\n<p>\uc704 \ucf54\ub4dc\uc5d0\uc11c\ub294 <code>scikit-learn<\/code>\uc758 <code>DecisionTreeClassifier<\/code>\ub97c \uc0ac\uc6a9\ud574 XOR \ubb38\uc81c\ub97c \ud574\uacb0\ud55c \ud6c4, \uadf8 \uacb0\uacfc\ub97c \ud30c\uc774\ud1a0\uce58 \ud150\uc11c\ub85c \ubcc0\ud658\ud574 \ub525\ub7ec\ub2dd \ubaa8\ub378\uacfc \uacb0\ud569\ud560 \uc218 \uc788\ub294 \ud615\ud0dc\ub85c \ub9cc\ub4e4\uc5c8\uc2b5\ub2c8\ub2e4. \uc2dc\uac01\ud654\ub97c \ucd94\uac00\ud558\uc5ec \uac01 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc640 \ud074\ub798\uc2a4 \ub808\uc774\ube14\uc744 \uc2dc\uac01\uc801\uc73c\ub85c \ud655\uc778\ud560 \uc218 \uc788\ub3c4\ub85d \ud588\uc2b5\ub2c8\ub2e4. \uc774\ub7f0 \ubc29\uc2dd\uc73c\ub85c \uacb0\uc815 \ud2b8\ub9ac\uc758 \ucd9c\ub825 \uac12\uc744 \ub2e4\ub978 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\ub294 \ub4f1, \uacb0\uc815 \ud2b8\ub9ac\uc640 \ud30c\uc774\ud1a0\uce58 \ubaa8\ub378\uc744 \uacb0\ud569\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\uacb0\uc815 \ud2b8\ub9ac \uad6c\uc870 \uc2dc\uac01\ud654\ud558\uae30<\/h3>\n<p>\uacb0\uc815 \ud2b8\ub9ac\uc758 \ud559\uc2b5 \uacb0\uacfc\ub97c \ub354 \uc798 \uc774\ud574\ud558\uae30 \uc704\ud574\uc11c\ub294 \uacb0\uc815 \ud2b8\ub9ac \uc790\uccb4\uc758 \uad6c\uc870\ub97c \uc2dc\uac01\ud654\ud558\ub294 \uac83\ub3c4 \uc911\uc694\ud569\ub2c8\ub2e4. <code>scikit-learn<\/code>\uc758 <code>plot_tree()<\/code> \ud568\uc218\ub97c \uc774\uc6a9\ud558\uba74 \uacb0\uc815 \ud2b8\ub9ac\uc758 \ubd84\uae30 \uacfc\uc815\uc744 \uc2dc\uac01\uc801\uc73c\ub85c \uc27d\uac8c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>from sklearn import datasets\nfrom sklearn.tree import DecisionTreeClassifier, plot_tree\nimport matplotlib.pyplot as plt\n\n# \ub370\uc774\ud130\uc14b \ub85c\ub4dc\niris = datasets.load_iris()\nX, y = iris.data, iris.target\n\n# \uacb0\uc815 \ud2b8\ub9ac \ubaa8\ub378 \uc0dd\uc131 \ubc0f \ud559\uc2b5\nmodel = DecisionTreeClassifier()\nmodel.fit(X, y)\n\n# \uacb0\uc815 \ud2b8\ub9ac \uc2dc\uac01\ud654\nplt.figure(figsize=(12, 6))\nplot_tree(model, filled=True, feature_names=iris.feature_names, class_names=iris.target_names)\nplt.title(\"\uacb0\uc815 \ud2b8\ub9ac \uc2dc\uac01\ud654\")\nplt.show()<\/code><\/pre>\n<p>\uc704 \ucf54\ub4dc\uc5d0\uc11c\ub294 <code>iris<\/code> \ub370\uc774\ud130\uc14b\uc744 \uc774\uc6a9\ud574 \uacb0\uc815 \ud2b8\ub9ac\ub97c \ud559\uc2b5\uc2dc\ud0a8 \ud6c4, <code>plot_tree()<\/code> \ud568\uc218\ub97c \uc0ac\uc6a9\ud574 \uacb0\uc815 \ud2b8\ub9ac\uc758 \uad6c\uc870\ub97c \uc2dc\uac01\ud654\ud588\uc2b5\ub2c8\ub2e4. \uc774 \uc2dc\uac01\ud654\ub97c \ud1b5\ud574 \uac01 \ub178\ub4dc\uc5d0\uc11c \uc5b4\ub5a4 \uae30\uc900\uc73c\ub85c \ub370\uc774\ud130\ub97c \ubd84\ud560\ud588\ub294\uc9c0, \uac01 \ub9ac\ud504 \ub178\ub4dc\uc5d0\uc11c \uc5b4\ub5a4 \ud074\ub798\uc2a4\uc5d0 \uc18d\ud558\ub294\uc9c0\ub97c \uba85\ud655\ud788 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uacb0\uc815 \ud2b8\ub9ac \ubaa8\ub378\uc758 \uacb0\uc815 \uacfc\uc815\uc744 \uc27d\uac8c \uc774\ud574\ud558\uace0 \uc124\uba85\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>4. \uacb0\uc815 \ud2b8\ub9ac\uc640 \uc2e0\uacbd\ub9dd\uc758 \uacb0\ud569<\/h2>\n<p>\uacb0\uc815 \ud2b8\ub9ac\uc640 \uc2e0\uacbd\ub9dd\uc744 \ud568\uaed8 \uc0ac\uc6a9\ud558\uba74 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub354\uc6b1 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uacb0\uc815 \ud2b8\ub9ac\ub294 \ub370\uc774\ud130\ub97c \uc804\ucc98\ub9ac\ud558\uac70\ub098 \ud2b9\uc131\uc744 \uc120\ud0dd\ud558\ub294 \ub370 \uc720\uc6a9\ud558\uace0, \uc774\ud6c4 \ud30c\uc774\ud1a0\uce58\ub85c \uad6c\uc131\ub41c \uc2e0\uacbd\ub9dd\uc740 \ube44\uc120\ud615 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \ub6f0\uc5b4\ub09c \uc131\ub2a5\uc744 \ubcf4\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc0ac\uc6a9\ud574 \uc8fc\uc694 \ud2b9\uc131\uc744 \ucd94\ucd9c\ud55c \ud6c4, \uc774 \ud2b9\uc131\uc744 \ud30c\uc774\ud1a0\uce58 \uc2e0\uacbd\ub9dd\uc5d0 \uc785\ub825\ud558\uc5ec \ucd5c\uc885 \uc608\uce21\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>\uc608\uc81c: \uacb0\uc815 \ud2b8\ub9ac \ucd9c\ub825\uac12\uc744 \uc2e0\uacbd\ub9dd \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud558\uae30<\/h3>\n<pre><code>import torch.nn as nn\nimport torch.optim as optim\n\n# \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \uc815\uc758\nclass SimpleNN(nn.Module):\n    def __init__(self):\n        super(SimpleNN, self).__init__()\n        self.fc1 = nn.Linear(2, 4)\n        self.fc2 = nn.Linear(4, 1)\n\n    def forward(self, x):\n        x = torch.relu(self.fc1(x))\n        x = torch.sigmoid(self.fc2(x))\n        return x\n\n# \uc2e0\uacbd\ub9dd \ubaa8\ub378 \uc0dd\uc131\nnn_model = SimpleNN()\ncriterion = nn.BCELoss()\noptimizer = optim.SGD(nn_model.parameters(), lr=0.01)\n\n# \uacb0\uc815 \ud2b8\ub9ac\uc758 \uc608\uce21\uac12\uc744 \uc2e0\uacbd\ub9dd\uc758 \ud559\uc2b5 \ub370\uc774\ud130\ub85c \uc0ac\uc6a9\ninputs = tensor_X\nlabels = tensor_predictions.unsqueeze(1)\n\n# \ud559\uc2b5 \uacfc\uc815\nfor epoch in range(100):\n    optimizer.zero_grad()\n    outputs = nn_model(inputs)\n    loss = criterion(outputs, labels)\n    loss.backward()\n    optimizer.step()\n\n    if (epoch + 1) % 10 == 0:\n        print(f'Epoch [{epoch+1}\/100], Loss: {loss.item():.4f}')\n\n# \ud559\uc2b5 \uacb0\uacfc \uc2dc\uac01\ud654\nplt.figure(figsize=(8, 6))\nwith torch.no_grad():\n    outputs = nn_model(inputs).squeeze().numpy()\n    for i, (x, label, output) in enumerate(zip(X, y, outputs)):\n        plt.scatter(x[0], x[1], c='red' if output &lt; 0.5 else 'blue', marker='x' if label == 0 else 'o', label=f'Predicted Class {int(output &gt;= 0.5)}' if i &lt; 2 else \"\")\n\nplt.xlabel('Feature 1')\nplt.ylabel('Feature 2')\nplt.title('Neural Network Predictions After Training')\nplt.legend()\nplt.grid(True)\nplt.show()<\/code><\/pre>\n<p>\uc704 \uc608\uc81c\uc5d0\uc11c\ub294 \uac04\ub2e8\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc815\uc758\ud558\uace0, \uacb0\uc815 \ud2b8\ub9ac\uc758 \uc608\uce21 \uac12\uc744 \uc2e0\uacbd\ub9dd\uc758 \uc785\ub825 \ub370\uc774\ud130\ub85c \uc0ac\uc6a9\ud574 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. \ud559\uc2b5 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uc5ec \uac01 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \uc608\uce21 \ud074\ub798\uc2a4 \ub808\uc774\ube14\uc744 \uc2dc\uac01\uc801\uc73c\ub85c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uacb0\uc815 \ud2b8\ub9ac\uc640 \uc2e0\uacbd\ub9dd\uc774 \uacb0\ud569\ub41c \ud615\ud0dc\uc758 \ubaa8\ub378\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>5. \uacb0\ub860<\/h2>\n<p>\uacb0\uc815 \ud2b8\ub9ac\ub294 \uac04\ub2e8\ud558\uba74\uc11c\ub3c4 \uac15\ub825\ud55c \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\ub85c, \ub370\uc774\ud130\uc758 \uad6c\uc870\ub97c \uc27d\uac8c \uc774\ud574\ud558\uace0 \uc124\uba85\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud1a0\uce58\uc640 \uac19\uc740 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc640 \uacb0\ud569\ud558\uba74, \uacb0\uc815 \ud2b8\ub9ac\uc758 \uac15\uc810\uacfc \uc2e0\uacbd\ub9dd\uc758 \ube44\uc120\ud615 \ubb38\uc81c \ud574\uacb0 \ub2a5\ub825\uc744 \ud568\uaed8 \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ubc88 \uae00\uc5d0\uc11c\ub294 \uacb0\uc815 \ud2b8\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150\ubd80\ud130 \ud30c\uc774\ud1a0\uce58\ub97c \uc0ac\uc6a9\ud55c \uad6c\ud604 \ubc29\ubc95\uae4c\uc9c0 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \uacb0\uc815 \ud2b8\ub9ac\uc640 \ud30c\uc774\ud1a0\uce58\uc758 \uacb0\ud569 \uac00\ub2a5\uc131\uc744 \uc774\ud574\ud558\uc168\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<p>\uacb0\uc815 \ud2b8\ub9ac\uc640 \ub525\ub7ec\ub2dd\uc758 \uacb0\ud569\uc740 \ub9e4\uc6b0 \ud765\ubbf8\ub85c\uc6b4 \uc5f0\uad6c \uc8fc\uc81c\uc774\uba70, \uc774\ub97c \uc2e4\uc81c \ud504\ub85c\uc81d\ud2b8\uc5d0 \uc801\uc6a9\ud560 \uc218 \uc788\ub294 \ub9ce\uc740 \uac00\ub2a5\uc131\uc774 \uc5f4\ub824 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc5d0\ub294 \uc559\uc0c1\ube14 \ud559\uc2b5 \uae30\ubc95\uacfc \ud30c\uc774\ud1a0\uce58\uc758 \uc751\uc6a9\uc5d0 \ub300\ud574 \ub2e4\ub904\ubcf4\ub294 \uac83\ub3c4 \uc88b\uc740 \uacf5\ubd80\uac00 \ub420 \uac83\uc785\ub2c8\ub2e4.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub525\ub7ec\ub2dd\uc774 \ub525\ub7ec\ub2dd\uc774\ub77c \ubd88\ub9ac\uae30 \uc804\uc5d0, \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ucd08 \uc911 \ud558\ub098\ub294 &#8220;\uacb0\uc815 \ud2b8\ub9ac(Decision Tree)&#8221; \ubaa8\ub378\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \uacb0\uc815 \ud2b8\ub9ac\ub294 \uc124\uba85\uc774 \uc9c1\uad00\uc801\uc774\uace0 \uad6c\ud604\uc774 \uc26c\uc6b4 \ud3b8\uc774\ub77c \uc785\ubb38\uc790\uc5d0\uac8c \uc801\ud569\ud55c \ud559\uc2b5 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uacb0\uc815 \ud2b8\ub9ac\uac00 \ubb34\uc5c7\uc778\uc9c0, \uadf8\ub9ac\uace0 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc778 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud574 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc5b4\ub5bb\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294\uc9c0 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub860\uc801 \ubc30\uacbd\ubd80\ud130 \uc2dc\uc791\ud574 \ud30c\uc774\ud1a0\uce58\ub85c\uc758 \uad6c\ud604\uae4c\uc9c0, \ub2e4\uc591\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \uc774\ud574\ud558\uae30 \uc27d\uac8c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uacb0\uc815 \ud2b8\ub9ac\ub780? \uacb0\uc815 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29998\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uacb0\uc815 \ud2b8\ub9ac&#8221;<\/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":[33],"tags":[],"class_list":["post-29998","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uacb0\uc815 \ud2b8\ub9ac - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/29998\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uacb0\uc815 \ud2b8\ub9ac - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\ub525\ub7ec\ub2dd\uc774 \ub525\ub7ec\ub2dd\uc774\ub77c \ubd88\ub9ac\uae30 \uc804\uc5d0, \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ucd08 \uc911 \ud558\ub098\ub294 &#8220;\uacb0\uc815 \ud2b8\ub9ac(Decision Tree)&#8221; \ubaa8\ub378\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \uacb0\uc815 \ud2b8\ub9ac\ub294 \uc124\uba85\uc774 \uc9c1\uad00\uc801\uc774\uace0 \uad6c\ud604\uc774 \uc26c\uc6b4 \ud3b8\uc774\ub77c \uc785\ubb38\uc790\uc5d0\uac8c \uc801\ud569\ud55c \ud559\uc2b5 \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uacb0\uc815 \ud2b8\ub9ac\uac00 \ubb34\uc5c7\uc778\uc9c0, \uadf8\ub9ac\uace0 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\uc778 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \uc774\uc6a9\ud574 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc5b4\ub5bb\uac8c \uad6c\ud604\ud560 \uc218 \uc788\ub294\uc9c0 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub860\uc801 \ubc30\uacbd\ubd80\ud130 \uc2dc\uc791\ud574 \ud30c\uc774\ud1a0\uce58\ub85c\uc758 \uad6c\ud604\uae4c\uc9c0, \ub2e4\uc591\ud55c \uc608\uc81c\ub97c \ud1b5\ud574 \uc774\ud574\ud558\uae30 \uc27d\uac8c \uc124\uba85\ud558\uaca0\uc2b5\ub2c8\ub2e4. 1. \uacb0\uc815 \ud2b8\ub9ac\ub780? \uacb0\uc815 &hellip; \ub354 \ubcf4\uae30 &quot;\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \uacb0\uc815 \ud2b8\ub9ac&quot;\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29998\/\" \/>\n<meta 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