\n pip install torch numpy scikit-learn\n <\/code>\n <\/pre>\n3.2 \ub370\uc774\ud130\uc14b \uc900\ube44<\/h3>\n
\n \uc720\ubc29\uc554 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud558\uc5ec KNN\uc744 \uad6c\ud604\ud560 \uac83\uc785\ub2c8\ub2e4.
\n scikit-learn\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \uc720\ubc29\uc554 \ub370\uc774\ud130\uc14b\uc744 \ubd88\ub7ec\uc624\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \n import numpy as np\n import torch\n from sklearn.datasets import load_breast_cancer\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n \n # \ub370\uc774\ud130\uc14b \ub85c\ub4dc\n data = load_breast_cancer()\n X = data.data\n y = data.target\n \n # \ub370\uc774\ud130\uc14b \ubd84\ud560 (\ud6c8\ub828\/\ud14c\uc2a4\ud2b8)\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n \n # \ub370\uc774\ud130 \uc815\uaddc\ud654\n scaler = StandardScaler()\n X_train = scaler.fit_transform(X_train)\n X_test = scaler.transform(X_test)\n <\/code>\n <\/pre>\n3.3 KNN \uc54c\uace0\ub9ac\uc998 \uad6c\ud604<\/h3>\n
\n \uc774\uc81c KNN \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n \uba3c\uc800, KNN\uc744 \uc218\ud589\ud558\ub294 \ud074\ub798\uc2a4\ub97c \uc815\uc758\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \n class KNN:\n def __init__(self, k=3):\n self.k = k\n \n def fit(self, X, y):\n self.X_train = X\n self.y_train = y\n \n def predict(self, X):\n distances = []\n \n for x in X:\n distance = np.sqrt(np.sum((self.X_train - x) ** 2, axis=1))\n distances.append(distance)\n \n distances = np.array(distances)\n neighbors = np.argsort(distances)[:, :self.k]\n return np.array([self.y_train[neighbor].mode()[0] for neighbor in neighbors])\n <\/code>\n <\/pre>\n3.4 \ubaa8\ub378 \ud559\uc2b5 \ubc0f \uc608\uce21<\/h3>\n
\n KNN \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \uacfc\uc815\uc744 \ubcf4\uc5ec\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4.\n <\/p>\n
\n \n # KNN \ubaa8\ub378 \uc0dd\uc131\n knn = KNN(k=3)\n \n # \ud6c8\ub828 \ub370\uc774\ud130\ub85c \ubaa8\ub378 \uc801\ud569\n knn.fit(X_train, y_train)\n \n # \ud14c\uc2a4\ud305 \ub370\uc774\ud130\ub85c \uc608\uce21\n predictions = knn.predict(X_test)\n \n # \uc815\ud655\ub3c4 \uacc4\uc0b0\n accuracy = np.mean(predictions == y_test)\n print(f'\ubaa8\ub378 \uc815\ud655\ub3c4: {accuracy * 100:.2f}%')\n <\/code>\n <\/pre>\n4. KNN\uc744 \uac1c\uc120\ud558\uae30<\/h2>\n
\n KNN\uc758 \uc131\ub2a5\uc744 \ud5a5\uc0c1\uc2dc\ud0ac \uc218 \uc788\ub294 \uba87 \uac00\uc9c0 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n \uc608\ub97c \ub4e4\uc5b4, K \uac12\uc744 \uc870\uc815\ud558\uac70\ub098 \uac70\ub9ac \ucc99\ub3c4\ub97c \ubcc0\uacbd\ud558\ub294 \ubc29\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4.
\n \ub610\ud55c, \ub370\uc774\ud130\uc758 \ucc28\uc6d0\uc744 \ucd95\uc18c\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
4.1 K \uac12 \uc870\uc815<\/h3>\n
\n K \uac12\uc740 KNN \uc54c\uace0\ub9ac\uc998\uc758 \uc131\ub2a5\uc5d0 \ud06c\uac8c \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4.
\n K \uac12\uc744 \ub108\ubb34 \uc791\uac8c \uc124\uc815\ud558\uba74 \uacfc\uc801\ud569(overfitting)\uc774 \ubc1c\uc0dd\ud560 \uc218 \uc788\uc73c\uba70,
\n \ub108\ubb34 \ud06c\uac8c \uc124\uc815\ud558\uba74 \uc77c\ubc18\ud654 \uc131\ub2a5\uc774 \ub5a8\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \ub530\ub77c\uc11c Cross-Validation \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 K \uac12\uc744 \ucc3e\uc544\uc57c \ud569\ub2c8\ub2e4.\n <\/p>\n
4.2 \uac70\ub9ac \ucc99\ub3c4 \ubcc0\uacbd<\/h3>\n
\n \uc720\ud074\ub9ac\ub4dc \uac70\ub9ac \uc678\uc5d0\ub3c4 \ub9e8\ud558\ud0c4 \uac70\ub9ac, \ubbf8\uc138\ud55c \uac70\ub9ac \ub4f1\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc2e4\ud5d8\uc744 \ud1b5\ud574 \uac00\uc7a5 \uc801\uc808\ud55c \uac70\ub9ac \uce21\uc815 \ubc29\ubc95\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n <\/p>\n
4.3 \ucc28\uc6d0 \ucd95\uc18c<\/h3>\n
\n PCA(\uc8fc\uc131\ubd84 \ubd84\uc11d)\uc640 \uac19\uc740 \ucc28\uc6d0 \ucd95\uc18c \uae30\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc758 \ucc28\uc6d0\uc744 \uc904\uc774\uba74
\n KNN\uc758 \uc131\ub2a5\uc774 \ud5a5\uc0c1\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \ucc28\uc6d0\uc774 \ub192\uc740 \uacbd\uc6b0 \ub370\uc774\ud130\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \uc774\ud574\ud558\uae30 \uc5b4\ub824\uc6b8 \ubfd0 \uc544\ub2c8\ub77c,
\n \uacc4\uc0b0\uc758 \ubcf5\uc7a1\uc131\ub3c4 \uc99d\uac00\ud558\uac8c \ub429\ub2c8\ub2e4.\n <\/p>\n
5. KNN\uacfc \ub525\ub7ec\ub2dd\uc758 \uad00\uacc4<\/h2>\n
\n KNN \uc54c\uace0\ub9ac\uc998\uc740 \ub525\ub7ec\ub2dd\uacfc \ud568\uaed8 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc608\ub97c \ub4e4\uc5b4, \ub525\ub7ec\ub2dd \ubaa8\ub378\uc758 \ucd9c\ub825\uc744 KNN\uc758 \uae30\ubcf8 \ub808\uc774\uc5b4\uc5d0 \uc5f0\uacb0\ud558\uc5ec
\n \ubcf4\ub2e4 \ud6a8\uc728\uc801\uc778 \ubd84\ub958\uae30(classifier)\ub85c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \ub610\ud55c, KNN\uc758 \uc774\uc6c3 \ub370\uc774\ud130\uc5d0\uc11c \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\uc5ec \ub525\ub7ec\ub2dd\uc758 \ud2b9\uc131(feature)\uc73c\ub85c \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n <\/p>\n
6. \ub9c8\ubb34\ub9ac<\/h2>\n
\n K-\ucd5c\uadfc\uc811 \uc774\uc6c3(KNN)\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \uae30\ubcf8\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c,
\n \uadf8 \uad6c\ud604\uacfc \uc774\ud574\uac00 \ub9e4\uc6b0 \uc27d\uc2b5\ub2c8\ub2e4.
\n \ud558\uc9c0\ub9cc \uc54c\uace0\ub9ac\uc998\uc758 \ub2e8\uc810\ub4e4, \ud2b9\ud788 \ub300\uaddc\ubaa8 \ub370\uc774\ud130\uc14b\uacfc \uace0\ucc28\uc6d0 \ub370\uc774\ud130\uc5d0\uc11c\uc758
\n \uc131\ub2a5 \ubb38\uc81c\ub97c \uc774\ud574\ud558\uace0 \uc774\ub97c \uac1c\uc120\ud558\ub294 \ubc29\ubc95\uc744 \uc544\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.
\n \uc774 \uae00\uc744 \ud1b5\ud574 KNN\uc5d0 \ub300\ud55c \uae30\ucd08 \uc9c0\uc2dd\uc744 \uc313\uace0,
\n PyTorch\ub97c \ud1b5\ud574 KNN\uc744 \uc2e4\uc81c\ub85c \uad6c\ud604\ud574\ubcf4\ub294 \uae30\ud68c\ub97c \uac00\uc9c0\uc168\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n <\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
K-\ucd5c\uadfc\uc811 \uc774\uc6c3(K-Nearest Neighbors, KNN)\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc5d0\uc11c \ub9e4\uc6b0 \uac04\ub2e8\ud558\uace0 \uc9c1\uad00\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c, \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc5d0 \ub300\ud574 \uac00\uc7a5 \uac00\uae4c\uc6b4 K\uac1c\uc758 \uc774\uc6c3\uc744 \ucc3e\uace0 \uadf8 \uc774\uc6c3\ub4e4\uc758 \ub77c\ubca8\uc5d0 \ub530\ub77c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. KNN\uc740 \uc8fc\ub85c \ubd84\ub958 \ubb38\uc81c\uc5d0 \uc0ac\uc6a9\ub418\uc9c0\ub9cc \ud68c\uadc0 \ubb38\uc81c\uc5d0\ub3c4 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. KNN\uc758 \uae30\ubcf8 \uc6d0\ub9ac KNN \uc54c\uace0\ub9ac\uc998\uc758 \uae30\ubcf8\uc801\uc778 \uc544\uc774\ub514\uc5b4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \uc0d8\ud50c\uc744 \ubd84\ub958\ud558\uace0\uc790 \ud560 \ub54c, \ud574\ub2f9 \uc0d8\ud50c\uacfc \uac00\uc7a5 \uac00\uae4c\uc6b4 … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, K-\ucd5c\uadfc\uc811 \uc774\uc6c3”<\/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-29918","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, K-\ucd5c\uadfc\uc811 \uc774\uc6c3 - \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\/29918\/\" \/>\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, K-\ucd5c\uadfc\uc811 \uc774\uc6c3 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"K-\ucd5c\uadfc\uc811 \uc774\uc6c3(K-Nearest Neighbors, KNN)\uc740 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc5d0\uc11c \ub9e4\uc6b0 \uac04\ub2e8\ud558\uace0 \uc9c1\uad00\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c, \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc5d0 \ub300\ud574 \uac00\uc7a5 \uac00\uae4c\uc6b4 K\uac1c\uc758 \uc774\uc6c3\uc744 \ucc3e\uace0 \uadf8 \uc774\uc6c3\ub4e4\uc758 \ub77c\ubca8\uc5d0 \ub530\ub77c \uc608\uce21\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. KNN\uc740 \uc8fc\ub85c \ubd84\ub958 \ubb38\uc81c\uc5d0 \uc0ac\uc6a9\ub418\uc9c0\ub9cc \ud68c\uadc0 \ubb38\uc81c\uc5d0\ub3c4 \uc801\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. 1. KNN\uc758 \uae30\ubcf8 \uc6d0\ub9ac KNN \uc54c\uace0\ub9ac\uc998\uc758 \uae30\ubcf8\uc801\uc778 \uc544\uc774\ub514\uc5b4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \uc8fc\uc5b4\uc9c4 \uc0d8\ud50c\uc744 \ubd84\ub958\ud558\uace0\uc790 \ud560 \ub54c, \ud574\ub2f9 \uc0d8\ud50c\uacfc \uac00\uc7a5 \uac00\uae4c\uc6b4 … \ub354 \ubcf4\uae30 "\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, K-\ucd5c\uadfc\uc811 \uc774\uc6c3"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/29918\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-28T03:18:39+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T06:50:52+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/29918\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29918\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, K-\ucd5c\uadfc\uc811 \uc774\uc6c3\",\"datePublished\":\"2024-10-28T03:18:39+00:00\",\"dateModified\":\"2024-11-26T06:50:52+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/29918\/\"},\"wordCount\":57,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ud30c\uc774\ud1a0\uce58 \uac15\uc88c\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/29918\/\",\"url\":\"https:\/\/atmokpo.com\/w\/29918\/\",\"name\":\"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, K-\ucd5c\uadfc\uc811 \uc774\uc6c3 - 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