{"id":477783,"date":"2023-08-09T09:20:08","date_gmt":"2023-08-09T09:20:08","guid":{"rendered":""},"modified":"2023-09-05T11:15:24","modified_gmt":"2023-09-05T11:15:24","slug":"k-nn-k-nearest-neighbours","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/kr\/wiki\/k-nn-k-nearest-neighbours\/","title":{"rendered":"k-NN(k-\ucd5c\uadfc\uc811 \uc774\uc6c3)"},"content":{"rendered":"<p>k-NN(k-Nearest Neighbours)\uc5d0 \ub300\ud55c \uac04\ub7b5\ud55c \uc815\ubcf4<\/p>\n<p>k-NN(k-Nearest Neighbors)\uc740 \ubd84\ub958 \ubc0f \ud68c\uadc0\uc5d0 \uc0ac\uc6a9\ub418\ub294 \ub2e8\uc21c\ud558\uace0 \ube44\ubaa8\uc218\uc801\uc774\uba70 \uac8c\uc73c\ub978 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ubd84\ub958 \ubb38\uc81c\uc5d0\uc11c k-NN\uc740 \uac1d\uccb4\uc758 &#039;k&#039; \uac00\uc7a5 \uac00\uae4c\uc6b4 \uc774\uc6c3 \uc911 \ub300\ubd80\ubd84\uc758 \ud074\ub798\uc2a4 \ub808\uc774\ube14\uc744 \uae30\ubc18\uc73c\ub85c \ud074\ub798\uc2a4 \ub808\uc774\ube14\uc744 \ud560\ub2f9\ud569\ub2c8\ub2e4. \ud68c\uadc0\uc758 \uacbd\uc6b0 &#039;k&#039; \ucd5c\uadfc\uc811 \uc774\uc6c3 \uac12\uc758 \ud3c9\uade0 \ub610\ub294 \uc911\uc559\uac12\uc744 \uae30\uc900\uc73c\ub85c \uac12\uc744 \ud560\ub2f9\ud569\ub2c8\ub2e4.<\/p>\n<h2>k-NN(k-Nearest Neighbours)\uc758 \uc720\ub798\uc640 \ucd5c\ucd08 \uc5b8\uae09\uc758 \uc5ed\uc0ac<\/h2>\n<p>k-NN \uc54c\uace0\ub9ac\uc998\uc740 \ud1b5\uacc4\uc801 \ud328\ud134 \uc778\uc2dd \ubb38\ud5cc\uc5d0 \ubfcc\ub9ac\ub97c \ub450\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uac1c\ub150\uc740 1951\ub144 Evelyn Fix\uc640 Joseph Hodges\uc5d0 \uc758\ud574 \ub3c4\uc785\ub418\uc5b4 \uae30\uc220\uc758 \uc2dc\ucd08\uac00 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8 \uc774\ud6c4\ub85c \uc774 \ubc29\ubc95\uc740 \ub2e8\uc21c\uc131\uacfc \ud6a8\uc728\uc131\uc73c\ub85c \uc778\ud574 \ub2e4\uc591\ud55c \uc601\uc5ed\uc5d0\uc11c \ub110\ub9ac \uc0ac\uc6a9\ub418\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>k-NN(k-Nearest Neighbours)\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \uc815\ubcf4\uc785\ub2c8\ub2e4. k-NN(k-Nearest Neighbours) \uc8fc\uc81c \ud655\uc7a5<\/h2>\n<p>k-NN\uc740 \uc8fc\uc5b4\uc9c4 \uc785\ub825\uc5d0 \uac00\uc7a5 \uac00\uae4c\uc6b4 &#039;k&#039; \ud559\uc2b5 \uc608\uc81c\ub97c \uc2dd\ubcc4\ud558\uace0 \ub2e4\uc218\uacb0 \uaddc\uce59 \ub610\ub294 \ud3c9\uade0\uc744 \uae30\ubc18\uc73c\ub85c \uc608\uce21\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\ub3d9\ud569\ub2c8\ub2e4. \uc720\ud074\ub9ac\ub4dc \uac70\ub9ac, \ub9e8\ud574\ud2bc \uac70\ub9ac, \ubbfc\ucf54\ud504\uc2a4\ud0a4 \uac70\ub9ac \ub4f1\uc758 \uac70\ub9ac \uce21\uc815\ubc95\uc740 \uc720\uc0ac\uc131\uc744 \uce21\uc815\ud558\ub294 \ub370 \uc790\uc8fc \uc0ac\uc6a9\ub429\ub2c8\ub2e4. k-NN\uc758 \uc8fc\uc694 \uad6c\uc131\uc694\uc18c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<ul>\n<li>&#039;k&#039; \uc120\ud0dd(\uace0\ub824\ud560 \uc774\uc6c3 \uc218)<\/li>\n<li>\uac70\ub9ac \uce21\uc815\ubc95(\uc608: \uc720\ud074\ub9ac\ub4dc, \ub9e8\ud574\ud2bc)<\/li>\n<li>\uacb0\uc815 \uaddc\uce59(\uc608: \ub2e4\uc218\uacb0 \ud22c\ud45c, \uac00\uc911\uce58 \ud22c\ud45c)<\/li>\n<\/ul>\n<h2>k-NN(k-Nearest Neighbours)\uc758 \ub0b4\ubd80 \uad6c\uc870. k-NN(k-\ucd5c\uadfc\uc811 \uc774\uc6c3) \uc791\ub3d9 \ubc29\uc2dd<\/h2>\n<p>k-NN\uc758 \uc791\ub3d9\uc740 \ub2e4\uc74c \ub2e8\uacc4\ub85c \ub098\ub20c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<ol>\n<li><strong>\uc22b\uc790 &#039;k&#039;\ub97c \uc120\ud0dd\ud558\uc138\uc694<\/strong> \u2013 \uace0\ub824\ud560 \uc774\uc6c3 \uc218\ub97c \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uac70\ub9ac \uce21\uc815\ubc95 \uc120\ud0dd<\/strong> \u2013 \uc778\uc2a4\ud134\uc2a4\uc758 &#039;\uadfc\uc811\uc131&#039;\uc744 \uce21\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uacb0\uc815\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>k-\ucd5c\uadfc\uc811\uc774\uc6c3 \ucc3e\uae30<\/strong> \u2013 \uc0c8 \uc778\uc2a4\ud134\uc2a4\uc5d0 \uac00\uc7a5 \uac00\uae4c\uc6b4 &#039;k&#039; \ud6c8\ub828 \uc0d8\ud50c\uc744 \uc2dd\ubcc4\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc608\uce21\ud558\ub2e4<\/strong> \u2013 \ubd84\ub958\uc5d0\ub294 \ub2e4\uc218\uacb0\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \ud68c\uadc0 \ubd84\uc11d\uc758 \uacbd\uc6b0 \ud3c9\uade0 \ub610\ub294 \uc911\uc559\uac12\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n<h2>k-NN(k-Nearest Neighbours)\uc758 \uc8fc\uc694 \ud2b9\uc9d5 \ubd84\uc11d<\/h2>\n<ul>\n<li><strong>\uac04\ub2e8<\/strong>: \uad6c\ud604\uacfc \uc774\ud574\uac00 \uc27d\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uc720\uc5f0\uc131<\/strong>: \ub2e4\uc591\ud55c \uac70\ub9ac \uce21\uc815\ubc95\uacfc \ud568\uaed8 \uc791\ub3d9\ud558\uba70 \ub2e4\uc591\ud55c \ub370\uc774\ud130 \uc720\ud615\uc5d0 \uc801\uc6a9 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\ud6c8\ub828 \ub2e8\uacc4 \uc5c6\uc74c<\/strong>: \uc608\uce21 \ub2e8\uacc4\uc5d0\uc11c \ud6c8\ub828 \ub370\uc774\ud130\ub97c \uc9c1\uc811 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc2dc\ub044\ub7ec\uc6b4 \ub370\uc774\ud130\uc5d0 \ubbfc\uac10\ud568<\/strong>: \uc774\uc0c1\uac12\uacfc \ub178\uc774\uc988\uac00 \uc131\ub2a5\uc5d0 \uc601\ud5a5\uc744 \uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\uacc4\uc0b0 \uc9d1\uc57d\uc801<\/strong>: \ud6c8\ub828 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \ubaa8\ub4e0 \uc0d8\ud50c\uae4c\uc9c0\uc758 \uac70\ub9ac\ub97c \uacc4\uc0b0\ud574\uc57c \ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>k-NN \uc720\ud615(k-\ucd5c\uadfc\uc811 \uc774\uc6c3)<\/h2>\n<p>k-NN\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \ubcc0\ud615\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<table>\n<thead>\n<tr>\n<th>\uc720\ud615<\/th>\n<th>\uc124\uba85<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\ud45c\uc900 k-NN<\/td>\n<td>\ubaa8\ub4e0 \uc774\uc6c3\uc5d0 \ub300\ud574 \uade0\uc77c\ud55c \uac00\uc911\uce58\ub97c \ud65c\uc6a9\ud569\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\uac00\uc911 k-NN<\/td>\n<td>\uc77c\ubc18\uc801\uc73c\ub85c \uac70\ub9ac\uc758 \uc5ed\uc218\ub97c \uae30\uc900\uc73c\ub85c \ub354 \uac00\uae4c\uc6b4 \uc774\uc6c3\uc5d0 \ub354 \ub9ce\uc740 \uac00\uc911\uce58\ub97c \ubd80\uc5ec\ud569\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\uc801\uc751\ud615 k-NN<\/td>\n<td>\uc785\ub825 \uacf5\uac04\uc758 \ub85c\uceec \uad6c\uc870\uc5d0 \ub530\ub77c &#039;k&#039;\ub97c \ub3d9\uc801\uc73c\ub85c \uc870\uc815\ud569\ub2c8\ub2e4.<\/td>\n<\/tr>\n<tr>\n<td>\uc9c0\uc5ed\uc801\uc73c\ub85c \uac00\uc911\ub41c k-NN<\/td>\n<td>\uc801\uc751\ud615 &#039;k&#039;\uc640 \uac70\ub9ac \uac00\uc911\uce58\ub97c \ubaa8\ub450 \uacb0\ud569\ud569\ub2c8\ub2e4.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>k-NN(k-Nearest Neighbours) \uc0ac\uc6a9\ubc29\ubc95\uacfc \uc0ac\uc6a9\uc5d0 \ub530\ub978 \ubb38\uc81c\uc810 \ubc0f \ud574\uacb0\ubc29\ubc95<\/h2>\n<ul>\n<li><strong>\uc6a9\ubc95<\/strong>: \ubd84\ub958, \ud68c\uadc0, \ucd94\ucc9c \uc2dc\uc2a4\ud15c, \uc774\ubbf8\uc9c0 \uc778\uc2dd.<\/li>\n<li><strong>\ubb38\uc81c<\/strong>: \ub192\uc740 \uacc4\uc0b0 \ube44\uc6a9, \uad00\ub828 \uc5c6\ub294 \uae30\ub2a5\uc5d0 \ubbfc\uac10, \ud655\uc7a5\uc131 \ubb38\uc81c.<\/li>\n<li><strong>\uc194\ub8e8\uc158<\/strong>: \ud2b9\uc9d5 \uc120\ud0dd, \uac70\ub9ac \uac00\uc911\uce58, KD-Tree\uc640 \uac19\uc740 \ud6a8\uc728\uc801\uc778 \ub370\uc774\ud130 \uad6c\uc870 \ud65c\uc6a9.<\/li>\n<\/ul>\n<h2>\uc8fc\uc694 \ud2b9\uc9d5 \ubc0f \uae30\ud0c0 \uc720\uc0ac \uc6a9\uc5b4\uc640\uc758 \ube44\uad50<\/h2>\n<table>\n<thead>\n<tr>\n<th>\uae30\uc778\ud558\ub2e4<\/th>\n<th>k-NN<\/th>\n<th>\uc758\uc0ac\uacb0\uc815 \ud2b8\ub9ac<\/th>\n<th>SVM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\ubaa8\ub378 \uc720\ud615<\/td>\n<td>\uac8c\uc73c\ub978 \ud559\uc2b5<\/td>\n<td>\uc5f4\uc815\uc801\uc778 \ud559\uc2b5<\/td>\n<td>\uc5f4\uc815\uc801\uc778 \ud559\uc2b5<\/td>\n<\/tr>\n<tr>\n<td>\ud6c8\ub828 \ubcf5\uc7a1\uc131<\/td>\n<td>\ub0ae\uc740<\/td>\n<td>\uc911\uac04<\/td>\n<td>\ub192\uc740<\/td>\n<\/tr>\n<tr>\n<td>\uc608\uce21 \ubcf5\uc7a1\uc131<\/td>\n<td>\ub192\uc740<\/td>\n<td>\ub0ae\uc740<\/td>\n<td>\uc911\uac04<\/td>\n<\/tr>\n<tr>\n<td>\uc18c\uc74c\uc5d0 \ub300\ud55c \ubbfc\uac10\ub3c4<\/td>\n<td>\ub192\uc740<\/td>\n<td>\uc911\uac04<\/td>\n<td>\ub0ae\uc740<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>k-NN(k-Nearest Neighbours)\uc5d0 \ub300\ud55c \ubbf8\ub798\uc758 \uad00\uc810\uacfc \uae30\uc220<\/h2>\n<p>\ud5a5\ud6c4 \ubc1c\uc804\uc740 \ube45 \ub370\uc774\ud130\uc5d0 \ub9de\uac8c k-NN\uc744 \ucd5c\uc801\ud654\ud558\uace0, \ub525 \ub7ec\ub2dd \ubaa8\ub378\uacfc \ud1b5\ud569\ud558\uace0, \ub178\uc774\uc988\uc5d0 \ub300\ud55c \uacac\uace0\uc131\uc744 \uac15\ud654\ud558\uace0, \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \uc120\ud0dd\uc744 \uc790\ub3d9\ud654\ud558\ub294 \ub370 \uc911\uc810\uc744 \ub458 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\ud504\ub85d\uc2dc \uc11c\ubc84\ub97c \uc0ac\uc6a9\ud558\uac70\ub098 k-NN(k-Nearest Neighbours)\uacfc \uc5f0\uacb0\ud558\ub294 \ubc29\ubc95<\/h2>\n<p>OneProxy\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \uac83\uacfc \uac19\uc740 \ud504\ub85d\uc2dc \uc11c\ubc84\ub294 \uc6f9 \uc2a4\ud06c\ub798\ud551 \ub610\ub294 \ub370\uc774\ud130 \uc218\uc9d1\uacfc \uad00\ub828\ub41c k-NN \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0\uc11c \uc5ed\ud560\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud504\ub85d\uc2dc\ub97c \ud1b5\ud574 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uba74 \uc775\uba85\uc131\uc774 \ubcf4\uc7a5\ub418\uace0 \uac15\ub825\ud55c k-NN \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uae30 \uc704\ud55c \ub354\uc6b1 \ub2e4\uc591\ud558\uace0 \ud3b8\uacac \uc5c6\ub294 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc81c\uacf5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>\uad00\ub828\ub41c \ub9c1\ud06c\ub4e4<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/neighbors.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn k-NN \ubb38\uc11c<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/K-nearest_neighbors_algorithm\" target=\"_new\" rel=\"noopener nofollow\">k-Nearest Neighbors \uc54c\uace0\ub9ac\uc998\uc5d0 \ub300\ud55c Wikipedia \ud398\uc774\uc9c0<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/kr\/\" target=\"_new\" rel=\"noopener\">OneProxy \u2013 \ud504\ub85d\uc2dc \uc11c\ubc84 \uc194\ub8e8\uc158<\/a><\/li>\n<\/ul>","protected":false},"featured_media":468739,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477783","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>k-NN (k-Nearest Neighbours)<\/mark>","faq_items":[{"question":"What is the k-Nearest Neighbours (k-NN) algorithm?","answer":"<p>The k-Nearest Neighbours (k-NN) is a simple and non-parametric algorithm used for classification and regression. It works by identifying the 'k' closest training examples to a given input and making predictions based on majority rule or averaging.<\/p>"},{"question":"What was the origin of the k-NN algorithm?","answer":"<p>The k-NN algorithm was introduced by Evelyn Fix and Joseph Hodges in 1951, marking its inception in statistical pattern recognition literature.<\/p>"},{"question":"How does the k-NN algorithm work?","answer":"<p>The k-NN algorithm works by choosing a number 'k', selecting a distance metric, finding the k-nearest neighbors to the new instance, and making a prediction based on majority voting for classification or computing the mean or median for regression.<\/p>"},{"question":"What are the key features of the k-NN algorithm?","answer":"<p>Key features of k-NN include its simplicity, flexibility, lack of a training phase, sensitivity to noisy data, and computational intensity.<\/p>"},{"question":"What are the different types of k-NN?","answer":"<p>There are various types of k-NN, including Standard k-NN, Weighted k-NN, Adaptive k-NN, and Locally Weighted k-NN.<\/p>"},{"question":"How can k-NN be used, and what are the related problems and solutions?","answer":"<p>k-NN can be used for classification, regression, recommender systems, and image recognition. Common problems include high computation cost, sensitivity to irrelevant features, and scalability issues. Solutions may involve feature selection, distance weighting, and utilizing efficient data structures like KD-Trees.<\/p>"},{"question":"How does the k-NN algorithm compare with other similar terms?","answer":"<p>k-NN differs from other algorithms like Decision Trees and SVM in aspects such as model type, training complexity, prediction complexity, and sensitivity to noise.<\/p>"},{"question":"What are the future prospects of k-NN?","answer":"<p>Future advancements in k-NN may focus on optimizing for big data, integrating with deep learning models, enhancing robustness to noise, and automating hyperparameter selection.<\/p>"},{"question":"How are proxy servers like OneProxy associated with k-NN?","answer":"<p>Proxy servers like OneProxy can be used in k-NN applications for web scraping or data collection. Gathering data through proxies ensures anonymity and can provide more diverse and unbiased datasets for building robust k-NN models.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/477783","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/wiki\/477783\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media\/468739"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/kr\/wp-json\/wp\/v2\/media?parent=477783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}