{"id":477558,"date":"2023-08-09T09:16:45","date_gmt":"2023-08-09T09:16:45","guid":{"rendered":""},"modified":"2023-09-05T11:14:58","modified_gmt":"2023-09-05T11:14:58","slug":"imbalanced-data","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/imbalanced-data\/","title":{"rendered":"Dengesiz veriler"},"content":{"rendered":"<p>Dengesiz veriler, veri analizi ve makine \u00f6\u011frenimi alan\u0131nda, bir veri k\u00fcmesi i\u00e7indeki s\u0131n\u0131flar\u0131n da\u011f\u0131l\u0131m\u0131n\u0131n olduk\u00e7a \u00e7arp\u0131k oldu\u011fu yayg\u0131n bir zorlu\u011fu ifade eder. Bu, bir s\u0131n\u0131f\u0131n (az\u0131nl\u0131k s\u0131n\u0131f\u0131) di\u011ferine (\u00e7o\u011funluk s\u0131n\u0131f\u0131) k\u0131yasla \u00f6nemli \u00f6l\u00e7\u00fcde daha az temsil edildi\u011fi anlam\u0131na gelir. Dengesiz veri sorunu, makine \u00f6\u011frenimi modelleri de dahil olmak \u00fczere \u00e7e\u015fitli veri odakl\u0131 uygulamalar\u0131n performans\u0131 ve do\u011frulu\u011fu \u00fczerinde derin bir etkiye sahip olabilir. G\u00fcvenilir ve tarafs\u0131z sonu\u00e7lar elde etmek i\u00e7in bu sorunun ele al\u0131nmas\u0131 \u00e7ok \u00f6nemlidir.<\/p>\n<h2>Dengesiz Verilerin K\u00f6keninin Tarihi ve \u0130lk S\u00f6z\u00fc<\/h2>\n<p>Dengesiz veri kavram\u0131 onlarca y\u0131ld\u0131r \u00e7e\u015fitli bilimsel alanlarda bir endi\u015fe kayna\u011f\u0131 olarak kabul edilmektedir. Bununla birlikte, makine \u00f6\u011frenimi toplulu\u011funa resmi giri\u015finin izi 1990&#039;lara kadar uzanabilir. Bu konuyu tart\u0131\u015fan ara\u015ft\u0131rma makaleleri ortaya \u00e7\u0131kmaya ba\u015flad\u0131; bu makaleler, konunun geleneksel \u00f6\u011frenme algoritmalar\u0131 a\u00e7\u0131s\u0131ndan yaratt\u0131\u011f\u0131 zorluklar\u0131 ve bu sorunla etkili bir \u015fekilde ba\u015fa \u00e7\u0131kmak i\u00e7in \u00f6zel tekniklere duyulan ihtiyac\u0131 vurgulad\u0131.<\/p>\n<h2>Dengesiz Veriler Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n<p>Dengesiz veriler, t\u0131bbi te\u015fhisler, sahtekarl\u0131k tespiti, anormallik tespiti ve nadir olay tahmini gibi \u00e7ok say\u0131da ger\u00e7ek d\u00fcnya senaryosunda ortaya \u00e7\u0131kar. Bu durumlarda, ilgilenilen olay, olay olmayan \u00f6rneklerle kar\u015f\u0131la\u015ft\u0131r\u0131ld\u0131\u011f\u0131nda genellikle nadirdir ve dengesiz s\u0131n\u0131f da\u011f\u0131l\u0131mlar\u0131na yol a\u00e7ar.<\/p>\n<p>Geleneksel makine \u00f6\u011frenimi algoritmalar\u0131 genellikle veri k\u00fcmesinin dengeli oldu\u011fu ve t\u00fcm s\u0131n\u0131flara e\u015fit davran\u0131ld\u0131\u011f\u0131 varsay\u0131m\u0131yla tasarlan\u0131r. Dengesiz verilere uyguland\u0131\u011f\u0131nda bu algoritmalar \u00e7o\u011funluk s\u0131n\u0131f\u0131n\u0131 tercih etme e\u011filiminde olur ve bu da az\u0131nl\u0131k s\u0131n\u0131f\u0131 \u00f6rneklerinin belirlenmesinde d\u00fc\u015f\u00fck performansa yol a\u00e7ar. Bu \u00f6nyarg\u0131n\u0131n ard\u0131ndaki neden, \u00f6\u011frenme s\u00fcrecinin, daha b\u00fcy\u00fck s\u0131n\u0131ftan b\u00fcy\u00fck \u00f6l\u00e7\u00fcde etkilenen genel do\u011fruluk taraf\u0131ndan y\u00f6nlendirilmesidir.<\/p>\n<h2>Dengesiz Verilerin \u0130\u00e7 Yap\u0131s\u0131: Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n<p>Dengesiz veriler a\u015fa\u011f\u0131daki gibi temsil edilebilir:<\/p>\n<pre><div class=\"bg-black rounded-md mb-4\"><div class=\"flex items-center relative text-gray-200 bg-gray-800 px-4 py-2 text-xs font-sans justify-between rounded-t-md\"><span>Lua<\/span><button class=\"flex ml-auto gap-2\"><svg stroke=\"currentColor\" fill=\"none\" stroke-width=\"2\" viewbox=\"0 0 24 24\" stroke-linecap=\"round\" stroke-linejoin=\"round\" class=\"h-4 w-4\" height=\"1em\" width=\"1em\" ><path d=\"M16 4h2a2 2 0 0 1 2 2v14a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V6a2 2 0 0 1 2-2h2\"><\/path><rect x=\"8\" y=\"2\" width=\"8\" height=\"4\" rx=\"1\" ry=\"1\"><\/rect><\/svg>Kodu kopyala<\/button><\/div><div class=\"p-4 overflow-y-auto\"><code class=\"!whitespace-pre hljs language-lua\" data-no-translation=\"\">|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n|       Class           |   Instances  |\n|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n|   Majority Class      |      N        |\n|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n|   Minority Class      |      M        |\n|<span class=\"hljs-comment\">----------------------- | ---------------|<\/span>\n<\/code><\/div><\/div><\/pre>\n<p>Burada N \u00e7o\u011funluk s\u0131n\u0131f\u0131ndaki \u00f6rneklerin say\u0131s\u0131n\u0131, M ise az\u0131nl\u0131k s\u0131n\u0131f\u0131ndaki \u00f6rneklerin say\u0131s\u0131n\u0131 temsil eder.<\/p>\n<h2>Dengesiz Verilerin Temel \u00d6zelliklerinin Analizi<\/h2>\n<p>Dengesiz verileri daha iyi anlamak i\u00e7in baz\u0131 temel \u00f6zellikleri analiz etmek \u00f6nemlidir:<\/p>\n<ol>\n<li>\n<p><strong>S\u0131n\u0131f Dengesizli\u011fi Oran\u0131<\/strong>: \u00c7o\u011funluk s\u0131n\u0131f\u0131ndaki \u00f6rneklerin az\u0131nl\u0131k s\u0131n\u0131f\u0131na oran\u0131. N\/M olarak ifade edilebilir.<\/p>\n<\/li>\n<li>\n<p><strong>Az\u0131nl\u0131k S\u0131n\u0131f\u0131n\u0131n Nadirli\u011fi<\/strong>: Veri k\u00fcmesindeki toplam \u00f6rnek say\u0131s\u0131na g\u00f6re az\u0131nl\u0131k s\u0131n\u0131f\u0131ndaki \u00f6rneklerin mutlak say\u0131s\u0131.<\/p>\n<\/li>\n<li>\n<p><strong>Veri \u00d6rt\u00fc\u015fmesi<\/strong>: Az\u0131nl\u0131k ve \u00e7o\u011funluk s\u0131n\u0131flar\u0131n\u0131n \u00f6zellik da\u011f\u0131l\u0131mlar\u0131 aras\u0131ndaki \u00f6rt\u00fc\u015fme derecesi. Daha fazla \u00f6rt\u00fc\u015fme, s\u0131n\u0131fland\u0131rmadaki zorlu\u011fun artmas\u0131na neden olabilir.<\/p>\n<\/li>\n<li>\n<p><strong>Maliyet Hassasiyeti<\/strong>: Farkl\u0131 s\u0131n\u0131flara farkl\u0131 yanl\u0131\u015f s\u0131n\u0131fland\u0131rma maliyetleri atama, dengeli bir s\u0131n\u0131fland\u0131rma elde etmek i\u00e7in az\u0131nl\u0131k s\u0131n\u0131f\u0131na daha fazla a\u011f\u0131rl\u0131k verme kavram\u0131.<\/p>\n<\/li>\n<\/ol>\n<h2>Dengesiz Veri T\u00fcrleri<\/h2>\n<p>S\u0131n\u0131f say\u0131s\u0131na ve s\u0131n\u0131f dengesizli\u011finin derecesine ba\u011fl\u0131 olarak farkl\u0131 t\u00fcrde dengesiz veriler vard\u0131r:<\/p>\n<h3>S\u0131n\u0131f Say\u0131s\u0131na G\u00f6re:<\/h3>\n<ol>\n<li>\n<p><strong>\u0130kili Dengesiz Veri<\/strong>: Yaln\u0131zca iki s\u0131n\u0131ftan olu\u015fan ve birinin say\u0131ca di\u011ferinden \u00f6nemli \u00f6l\u00e7\u00fcde \u00fcst\u00fcn oldu\u011fu bir veri k\u00fcmesi.<\/p>\n<\/li>\n<li>\n<p><strong>\u00c7ok S\u0131n\u0131fl\u0131 Dengesiz Veriler<\/strong>: En az biri di\u011ferlerine k\u0131yasla \u00f6nemli \u00f6l\u00e7\u00fcde daha az temsil edilen birden fazla s\u0131n\u0131fa sahip bir veri k\u00fcmesi.<\/p>\n<\/li>\n<\/ol>\n<h3>S\u0131n\u0131f Dengesizli\u011fi Derecesine G\u00f6re:<\/h3>\n<ol>\n<li>\n<p><strong>Orta Dengesizlik<\/strong>: Dengesizlik oran\u0131 nispeten d\u00fc\u015f\u00fckt\u00fcr, tipik olarak 1:2 ila 1:5 aras\u0131ndad\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>\u015eiddetli Dengesizlik<\/strong>: Dengesizlik oran\u0131 \u00e7ok y\u00fcksektir, s\u0131kl\u0131kla 1:10 veya daha fazlas\u0131n\u0131 a\u015far.<\/p>\n<\/li>\n<\/ol>\n<h2>Dengesiz Verileri Kullanma Yollar\u0131, Sorunlar ve \u00c7\u00f6z\u00fcmleri<\/h2>\n<h3>Dengesiz Verilerle \u0130lgili Sorunlar:<\/h3>\n<ol>\n<li>\n<p><strong>\u00d6nyarg\u0131l\u0131 S\u0131n\u0131fland\u0131rma<\/strong>: Model \u00e7o\u011funluk s\u0131n\u0131f\u0131n\u0131 destekleme e\u011filimindedir ve bu da az\u0131nl\u0131k s\u0131n\u0131f\u0131nda d\u00fc\u015f\u00fck performansa yol a\u00e7ar.<\/p>\n<\/li>\n<li>\n<p><strong>\u00d6\u011frenmede Zorluk<\/strong>: Geleneksel algoritmalar, s\u0131n\u0131rl\u0131 temsilleri nedeniyle nadir s\u0131n\u0131f \u00f6rneklerinden kal\u0131plar\u0131 \u00f6\u011frenmekte zorlan\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>Yan\u0131lt\u0131c\u0131 De\u011ferlendirme Metrikleri<\/strong>: Bir model yaln\u0131zca \u00e7o\u011funluk s\u0131n\u0131f\u0131n\u0131 tahmin ederek y\u00fcksek do\u011fruluk elde edebilece\u011finden do\u011fruluk yan\u0131lt\u0131c\u0131 bir \u00f6l\u00e7\u00fcm olabilir.<\/p>\n<\/li>\n<\/ol>\n<h3>\u00c7\u00f6z\u00fcmler:<\/h3>\n<ol>\n<li>\n<p><strong>Yeniden \u00d6rnekleme Teknikleri<\/strong>: \u00c7o\u011funluk s\u0131n\u0131f\u0131ndan d\u00fc\u015f\u00fck \u00f6rnekleme veya az\u0131nl\u0131k s\u0131n\u0131f\u0131ndan y\u00fcksek \u00f6rnekleme, veri k\u00fcmesinin dengelenmesine yard\u0131mc\u0131 olabilir.<\/p>\n<\/li>\n<li>\n<p><strong>Algoritmik Yakla\u015f\u0131mlar<\/strong>: Rastgele Orman, SMOTE ve ADASYN gibi dengesiz verileri i\u015flemek i\u00e7in tasarlanm\u0131\u015f \u00f6zel algoritmalar.<\/p>\n<\/li>\n<li>\n<p><strong>Maliyete Duyarl\u0131 \u00d6\u011frenme<\/strong>: Farkl\u0131 s\u0131n\u0131flara farkl\u0131 yanl\u0131\u015f s\u0131n\u0131fland\u0131rma maliyetleri atamak i\u00e7in \u00f6\u011frenme s\u00fcrecini de\u011fi\u015ftirmek.<\/p>\n<\/li>\n<li>\n<p><strong>Topluluk Y\u00f6ntemleri<\/strong>: Birden fazla s\u0131n\u0131fland\u0131r\u0131c\u0131n\u0131n birle\u015ftirilmesi dengesiz veriler \u00fczerinde genel performans\u0131 iyile\u015ftirebilir.<\/p>\n<\/li>\n<\/ol>\n<h2>Ana \u00d6zellikler ve Benzer Terimlerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>karakteristik<\/th>\n<th>Dengesiz Veriler<\/th>\n<th>Dengeli Veri<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>S\u0131n\u0131f Da\u011f\u0131l\u0131m\u0131<\/td>\n<td>\u00c7arpitilmis<\/td>\n<td>\u00dcniforma<\/td>\n<\/tr>\n<tr>\n<td>Meydan okumak<\/td>\n<td>\u00c7o\u011funluk s\u0131n\u0131f\u0131na y\u00f6nelik \u00f6nyarg\u0131<\/td>\n<td>T\u00fcm s\u0131n\u0131flara e\u015fit davran\u0131r<\/td>\n<\/tr>\n<tr>\n<td>Ortak \u00c7\u00f6z\u00fcmler<\/td>\n<td>Yeniden \u00f6rnekleme, Algoritmik ayarlamalar<\/td>\n<td>Standart \u00f6\u011frenme algoritmalar\u0131<\/td>\n<\/tr>\n<tr>\n<td>Performans Metrikleri<\/td>\n<td>Hassasiyet, Geri \u00c7a\u011f\u0131rma, F1 Puan\u0131<\/td>\n<td>Do\u011fruluk, Kesinlik, Geri \u00c7a\u011f\u0131rma<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Dengesiz Verilerle \u0130lgili Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n<p>Makine \u00f6\u011frenimi ara\u015ft\u0131rmalar\u0131 ilerledik\u00e7e, dengesiz verilerden kaynaklanan zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in daha geli\u015fmi\u015f tekniklerin ve algoritmalar\u0131n ortaya \u00e7\u0131kmas\u0131 muhtemeldir. Ara\u015ft\u0131rmac\u0131lar, dengesiz veri k\u00fcmeleri \u00fczerindeki modellerin performans\u0131n\u0131 art\u0131rmak ve onlar\u0131 ger\u00e7ek d\u00fcnya senaryolar\u0131na daha uyarlanabilir hale getirmek i\u00e7in s\u00fcrekli olarak yeni yakla\u015f\u0131mlar ara\u015ft\u0131r\u0131yor.<\/p>\n<h2>Proxy Sunucular\u0131 Nas\u0131l Kullan\u0131labilir veya Dengesiz Verilerle \u0130li\u015fkilendirilebilir?<\/h2>\n<p>Proxy sunucular\u0131, veri toplama, web kaz\u0131ma ve anonimle\u015ftirme dahil olmak \u00fczere \u00e7e\u015fitli veri yo\u011fun uygulamalarda hayati bir rol oynar. Dengesiz veri kavram\u0131yla do\u011frudan ilgili olmasa da, dengesiz veri k\u00fcmelerini i\u00e7erebilecek b\u00fcy\u00fck \u00f6l\u00e7ekli veri toplama g\u00f6revlerini ger\u00e7ekle\u015ftirmek i\u00e7in proxy sunucular kullan\u0131labilir. Proxy sunucular, IP adreslerini d\u00f6nd\u00fcrerek ve trafi\u011fi y\u00f6neterek IP yasaklar\u0131n\u0131n \u00f6nlenmesine yard\u0131mc\u0131 olur ve web sitelerinden veya API&#039;lerden daha sorunsuz veri \u00e7\u0131kar\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<p>Dengesiz veriler ve bunu gidermeye y\u00f6nelik teknikler hakk\u0131nda daha fazla bilgi i\u00e7in a\u015fa\u011f\u0131daki kaynaklar\u0131 ke\u015ffedebilirsiniz:<\/p>\n<ol>\n<li><a href=\"https:\/\/towardsdatascience.com\/dealing-with-imbalanced-data-in-machine-learning-7c4a692eda42\" target=\"_new\" rel=\"noopener nofollow\">Veri Bilimine Do\u011fru \u2013 Makine \u00d6\u011freniminde Dengesiz Verilerle Ba\u015fa \u00c7\u0131kmak<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/auto_examples\/applications\/plot_tomography_reconstruction.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn Belgeleri \u2013 Dengesiz Verileri Y\u00f6netme<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset\/\" target=\"_new\" rel=\"noopener nofollow\">Makine \u00d6\u011frenimi Ustal\u0131\u011f\u0131 \u2013 Makine \u00d6\u011frenimi Veri K\u00fcmenizdeki Dengesiz S\u0131n\u0131flarla M\u00fccadeleye Y\u00f6nelik Taktikler<\/a><\/li>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/5128907\" target=\"_new\" rel=\"noopener nofollow\">Bilgi ve Veri M\u00fchendisli\u011finde IEEE \u0130\u015flemleri - Dengesiz Verilerden \u00d6\u011frenme<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468603,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477558","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Imbalanced Data: A Comprehensive Guide<\/mark>","faq_items":[{"question":"Question: What is imbalanced data?","answer":"<p>Answer: Imbalanced data refers to a situation where the distribution of classes within a dataset is highly skewed, with one class (the minority class) being significantly underrepresented compared to another (the majority class). This can pose challenges in various data-driven applications, including machine learning, leading to biased classification and lower performance on the minority class.<\/p>"},{"question":"Question: How did the issue of imbalanced data originate?","answer":"<p>Answer: The concept of imbalanced data has been recognized as a concern in various fields for years. However, its formal introduction into the machine learning community can be traced back to the 1990s when research papers began highlighting the challenges it posed to traditional learning algorithms.<\/p>"},{"question":"Question: What are the key features of imbalanced data?","answer":"<p>Answer: Key features of imbalanced data include the class imbalance ratio, the rareness of the minority class, the degree of data overlap between classes, and cost sensitivity. These features influence the learning process and the performance of machine learning models.<\/p>"},{"question":"Question: What are the types of imbalanced data?","answer":"<p>Answer: Imbalanced data can be categorized based on the number of classes and the degree of class imbalance. Based on the number of classes, it can be binary (two classes) or multiclass (multiple classes). Based on the degree of class imbalance, it can be moderate or severe.<\/p>"},{"question":"Question: What are the problems with imbalanced data, and how can they be solved?","answer":"<p>Answer: The problems with imbalanced data include biased classification, difficulty in learning patterns from rare classes, and misleading evaluation metrics. To address these issues, various solutions can be employed, such as resampling techniques, algorithmic approaches, and cost-sensitive learning.<\/p>"},{"question":"Question: How can proxy servers be associated with imbalanced data?","answer":"<p>Answer: While not directly related to imbalanced data, proxy servers play a crucial role in data-intensive applications, including data collection and web scraping. They can be used to handle large-scale data collection tasks, which may involve imbalanced datasets, by rotating IP addresses and managing traffic to prevent IP bans and ensure smoother data extraction.<\/p>"},{"question":"Question: What are the future perspectives and technologies related to imbalanced data?","answer":"<p>Answer: As machine learning research progresses, more advanced techniques and algorithms are likely to emerge to address the challenges of imbalanced data. Researchers are continuously exploring novel approaches to enhance model performance on imbalanced datasets and make them more adaptable to real-world scenarios.<\/p>"},{"question":"Question: Where can I find more information about imbalanced data?","answer":"<p>Answer: For more in-depth information and resources about imbalanced data and techniques to address it, you can explore the provided links in the article, which include helpful articles, documentation, and research papers.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477558","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\/477558\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468603"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=477558"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}