{"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\/tr\/wiki\/k-nn-k-nearest-neighbours\/","title":{"rendered":"k-NN (k-En Yak\u0131n Kom\u015fular)"},"content":{"rendered":"<p>k-NN (k-En Yak\u0131n Kom\u015fular) hakk\u0131nda k\u0131sa bilgi<\/p>\n<p>k-En Yak\u0131n Kom\u015fular (k-NN), s\u0131n\u0131fland\u0131rma ve regresyon i\u00e7in kullan\u0131lan basit, parametrik olmayan ve tembel bir \u00f6\u011frenme algoritmas\u0131d\u0131r. S\u0131n\u0131fland\u0131rma problemlerinde k-NN, nesnenin &#039;k&#039; en yak\u0131n kom\u015fular\u0131 aras\u0131ndaki s\u0131n\u0131f etiketlerinin \u00e7o\u011funlu\u011funa dayal\u0131 olarak bir s\u0131n\u0131f etiketi atar. Regresyon i\u00e7in, &#039;k&#039; en yak\u0131n kom\u015fular\u0131n\u0131n de\u011ferlerinin ortalamas\u0131na veya medyan\u0131na dayal\u0131 bir de\u011fer atar.<\/p>\n<h2>K-NN&#039;nin (k-En Yak\u0131n Kom\u015fular) k\u00f6keninin tarihi ve ilk s\u00f6z\u00fc<\/h2>\n<p>K-NN algoritmas\u0131n\u0131n k\u00f6kleri istatistiksel \u00f6r\u00fcnt\u00fc tan\u0131ma literat\u00fcr\u00fcne dayanmaktad\u0131r. Konsept, 1951&#039;de Evelyn Fix ve Joseph Hodges taraf\u0131ndan tan\u0131t\u0131ld\u0131 ve tekni\u011fin ba\u015flang\u0131c\u0131 oldu. O zamandan beri basitli\u011fi ve etkinli\u011fi nedeniyle farkl\u0131 alanlarda yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/p>\n<h2>k-NN (k-En Yak\u0131n Kom\u015fular) hakk\u0131nda detayl\u0131 bilgi. Konunun geni\u015fletilmesi k-NN (k-En Yak\u0131n Kom\u015fular)<\/h2>\n<p>k-NN, belirli bir girdiye en yak\u0131n &#039;k&#039; e\u011fitim \u00f6rne\u011fini belirleyerek ve \u00e7o\u011funluk kural\u0131na veya ortalamaya dayal\u0131 tahminler yaparak \u00e7al\u0131\u015f\u0131r. \u00d6klid mesafesi, Manhattan mesafesi veya Minkowski mesafesi gibi mesafe \u00f6l\u00e7\u00fcmleri genellikle benzerli\u011fi \u00f6l\u00e7mek i\u00e7in kullan\u0131l\u0131r. k-NN&#039;nin temel bile\u015fenleri \u015funlard\u0131r:<\/p>\n<ul>\n<li>&#039;k&#039; se\u00e7imi (dikkate al\u0131nacak kom\u015fular\u0131n say\u0131s\u0131)<\/li>\n<li>Uzakl\u0131k \u00f6l\u00e7\u00fcs\u00fc (\u00f6r. \u00d6klidyen, Manhattan)<\/li>\n<li>Karar kural\u0131 (\u00f6rne\u011fin \u00e7o\u011funluk oylamas\u0131, a\u011f\u0131rl\u0131kl\u0131 oylama)<\/li>\n<\/ul>\n<h2>k-NN&#039;nin (k-En Yak\u0131n Kom\u015fular) i\u00e7 yap\u0131s\u0131. k-NN (k-En Yak\u0131n Kom\u015fular) nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h2>\n<p>K-NN&#039;nin \u00e7al\u0131\u015fmas\u0131 a\u015fa\u011f\u0131daki ad\u0131mlara ayr\u0131labilir:<\/p>\n<ol>\n<li><strong>&#039;K&#039; say\u0131s\u0131n\u0131 se\u00e7in<\/strong> \u2013 Dikkate al\u0131nacak kom\u015fular\u0131n say\u0131s\u0131n\u0131 se\u00e7in.<\/li>\n<li><strong>Bir mesafe \u00f6l\u00e7\u00fcm\u00fc se\u00e7in<\/strong> \u2013 \u00d6rneklerin &#039;yak\u0131nl\u0131\u011f\u0131n\u0131n&#039; nas\u0131l \u00f6l\u00e7\u00fclece\u011fini belirleyin.<\/li>\n<li><strong>K-en yak\u0131n kom\u015fular\u0131 bul<\/strong> \u2013 Yeni \u00f6rne\u011fe en yak\u0131n &#039;k&#039; e\u011fitim \u00f6rne\u011fini belirleyin.<\/li>\n<li><strong>Bir tahminde bulunun<\/strong> \u2013 S\u0131n\u0131fland\u0131rma i\u00e7in \u00e7o\u011funluk oyu kullan\u0131n. Regresyon i\u00e7in ortalamay\u0131 veya medyan\u0131 hesaplay\u0131n.<\/li>\n<\/ol>\n<h2>k-NN&#039;nin (k-En Yak\u0131n Kom\u015fular) temel \u00f6zelliklerinin analizi<\/h2>\n<ul>\n<li><strong>Basitlik<\/strong>: Uygulanmas\u0131 ve anla\u015f\u0131lmas\u0131 kolayd\u0131r.<\/li>\n<li><strong>Esneklik<\/strong>: \u00c7e\u015fitli mesafe metrikleriyle \u00e7al\u0131\u015f\u0131r ve farkl\u0131 veri t\u00fcrlerine uyarlanabilir.<\/li>\n<li><strong>E\u011fitim A\u015famas\u0131 Yok<\/strong>: Tahmin a\u015famas\u0131nda do\u011frudan e\u011fitim verilerini kullan\u0131r.<\/li>\n<li><strong>G\u00fcr\u00fclt\u00fcl\u00fc Verilere Kar\u015f\u0131 Hassas<\/strong>: Ayk\u0131r\u0131 de\u011ferler ve g\u00fcr\u00fclt\u00fc performans\u0131 etkileyebilir.<\/li>\n<li><strong>Hesaplama Yo\u011funlu\u011fu<\/strong>: E\u011fitim veri setindeki t\u00fcm numunelere olan mesafelerin hesaplanmas\u0131n\u0131 gerektirir.<\/li>\n<\/ul>\n<h2>k-NN T\u00fcrleri (k-En Yak\u0131n Kom\u015fular)<\/h2>\n<p>K-NN&#039;nin farkl\u0131 \u00e7e\u015fitleri vard\u0131r, \u00f6rne\u011fin:<\/p>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Standart k-NN<\/td>\n<td>T\u00fcm kom\u015fular i\u00e7in e\u015fit a\u011f\u0131rl\u0131k kullan\u0131r.<\/td>\n<\/tr>\n<tr>\n<td>A\u011f\u0131rl\u0131kl\u0131 k-NN<\/td>\n<td>Tipik olarak mesafenin tersi esas al\u0131narak, yak\u0131n kom\u015fulara daha fazla a\u011f\u0131rl\u0131k verilir.<\/td>\n<\/tr>\n<tr>\n<td>Uyarlanabilir k-NN<\/td>\n<td>Giri\u015f alan\u0131n\u0131n yerel yap\u0131s\u0131na g\u00f6re &#039;k&#039;yi dinamik olarak ayarlar.<\/td>\n<\/tr>\n<tr>\n<td>Yerel A\u011f\u0131rl\u0131kl\u0131 k-NN<\/td>\n<td>Hem uyarlanabilir &#039;k&#039; hem de mesafe a\u011f\u0131rl\u0131kland\u0131rmay\u0131 birle\u015ftirir.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>k-NN (k-En Yak\u0131n Kom\u015fular) kullan\u0131m yollar\u0131, kullan\u0131mla ilgili sorunlar ve \u00e7\u00f6z\u00fcmleri<\/h2>\n<ul>\n<li><strong>Kullan\u0131m<\/strong>: S\u0131n\u0131fland\u0131rma, Regresyon, \u00d6neri Sistemleri, G\u00f6r\u00fcnt\u00fc Tan\u0131ma.<\/li>\n<li><strong>Sorunlar<\/strong>: Y\u00fcksek hesaplama maliyeti, \u0130lgisiz \u00f6zelliklere kar\u015f\u0131 hassasl\u0131k, \u00d6l\u00e7eklenebilirlik sorunlar\u0131.<\/li>\n<li><strong>\u00c7\u00f6z\u00fcmler<\/strong>: \u00d6zellik se\u00e7imi, Uzakl\u0131k a\u011f\u0131rl\u0131kland\u0131rma, KD-Trees gibi verimli veri yap\u0131lar\u0131ndan yararlanma.<\/li>\n<\/ul>\n<h2>Ana \u00f6zellikler ve benzer terimlerle di\u011fer kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>Ba\u011flanmak<\/th>\n<th>k-NN<\/th>\n<th>Karar a\u011fa\u00e7lar\u0131<\/th>\n<th>DVM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Model t\u00fcr\u00fc<\/td>\n<td>Tembel \u00d6\u011frenme<\/td>\n<td>\u0130stekli \u00d6\u011frenme<\/td>\n<td>\u0130stekli \u00d6\u011frenme<\/td>\n<\/tr>\n<tr>\n<td>E\u011fitim Karma\u015f\u0131kl\u0131\u011f\u0131<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>Orta<\/td>\n<td>Y\u00fcksek<\/td>\n<\/tr>\n<tr>\n<td>Tahmin Karma\u015f\u0131kl\u0131\u011f\u0131<\/td>\n<td>Y\u00fcksek<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<td>Orta<\/td>\n<\/tr>\n<tr>\n<td>G\u00fcr\u00fclt\u00fcye Duyarl\u0131l\u0131k<\/td>\n<td>Y\u00fcksek<\/td>\n<td>Orta<\/td>\n<td>D\u00fc\u015f\u00fck<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>k-NN (k-En Yak\u0131n Kom\u015fular) ile ilgili gelece\u011fin perspektifleri ve teknolojileri<\/h2>\n<p>Gelecekteki geli\u015fmeler, k-NN&#039;yi b\u00fcy\u00fck veriler i\u00e7in optimize etmeye, derin \u00f6\u011frenme modelleriyle entegre etmeye, g\u00fcr\u00fclt\u00fcye kar\u015f\u0131 dayan\u0131kl\u0131l\u0131\u011f\u0131 art\u0131rmaya ve hiperparametre se\u00e7imini otomatikle\u015ftirmeye odaklanabilir.<\/p>\n<h2>Proxy sunucular\u0131 nas\u0131l kullan\u0131labilir veya k-NN (k-En Yak\u0131n Kom\u015fular) ile nas\u0131l ili\u015fkilendirilebilir?<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular, web kaz\u0131ma veya veri toplama i\u00e7eren k-NN uygulamalar\u0131nda rol oynayabilir. Proxy&#039;ler arac\u0131l\u0131\u011f\u0131yla veri toplamak, anonimli\u011fi sa\u011flar ve sa\u011flam k-NN modelleri olu\u015fturmak i\u00e7in daha \u00e7e\u015fitli ve tarafs\u0131z veri k\u00fcmeleri sa\u011flayabilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/neighbors.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn k-NN Belgeleri<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/K-nearest_neighbors_algorithm\" target=\"_new\" rel=\"noopener nofollow\">k-En Yak\u0131n Kom\u015fular Algoritmas\u0131 hakk\u0131ndaki Wikipedia sayfas\u0131<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy \u2013 Proxy Sunucu \u00c7\u00f6z\u00fcmleri<\/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\/tr\/wp-json\/wp\/v2\/wiki\/477783","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477783\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468739"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=477783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}