{"id":479372,"date":"2023-08-09T10:35:43","date_gmt":"2023-08-09T10:35:43","guid":{"rendered":""},"modified":"2023-09-05T11:18:40","modified_gmt":"2023-09-05T11:18:40","slug":"training-and-test-sets-in-machine-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/training-and-test-sets-in-machine-learning\/","title":{"rendered":"Makine \u00f6\u011freniminde e\u011fitim ve test setleri"},"content":{"rendered":"<p>Makine \u00f6\u011freniminde e\u011fitim ve test setleri hakk\u0131nda k\u0131sa bilgi<\/p>\n<p>Makine \u00f6\u011freniminde e\u011fitim ve test setleri, modelleri olu\u015fturmak, do\u011frulamak ve de\u011ferlendirmek i\u00e7in kullan\u0131lan \u00f6nemli bile\u015fenlerdir. E\u011fitim seti makine \u00f6\u011frenimi modelini \u00f6\u011fretmek i\u00e7in kullan\u0131l\u0131rken, test seti modelin performans\u0131n\u0131 \u00f6l\u00e7mek i\u00e7in kullan\u0131l\u0131r. Bu iki veri k\u00fcmesi birlikte, makine \u00f6\u011frenimi algoritmalar\u0131n\u0131n verimlili\u011fini ve etkinli\u011fini sa\u011flamada hayati bir rol oynar.<\/p>\n<h2>Makine \u00f6\u011freniminde e\u011fitim ve test setlerinin k\u00f6keninin tarihi ve ilk s\u00f6z\u00fc<\/h2>\n<p>Verileri e\u011fitim ve test setlerine ay\u0131rma kavram\u0131n\u0131n k\u00f6kleri istatistiksel modelleme ve do\u011frulama tekniklerine dayanmaktad\u0131r. Ara\u015ft\u0131rmac\u0131lar\u0131n g\u00f6r\u00fcnmeyen veriler \u00fczerinde modelleri de\u011ferlendirmenin \u00f6nemini fark etmesiyle 1970&#039;lerin ba\u015f\u0131nda makine \u00f6\u011freniminde tan\u0131t\u0131ld\u0131. Bu uygulama, bir modelin iyi bir \u015fekilde genelle\u015ftirilmesini ve a\u015f\u0131r\u0131 uyum olarak bilinen bir olgu olan yaln\u0131zca e\u011fitim verilerini ezberlememesini sa\u011flamaya yard\u0131mc\u0131 olur.<\/p>\n<h2>Makine \u00f6\u011freniminde e\u011fitim ve test setleri hakk\u0131nda detayl\u0131 bilgiler. Makine \u00f6\u011freniminde e\u011fitim ve test setleri konusunu geni\u015fletme<\/h2>\n<p>E\u011fitim ve test setleri, makine \u00f6\u011frenimi hatt\u0131n\u0131n ayr\u0131lmaz par\u00e7alar\u0131d\u0131r:<\/p>\n<ul>\n<li><strong>E\u011fitim Seti<\/strong>: Modeli e\u011fitmek i\u00e7in kullan\u0131l\u0131r. Hem giri\u015f verilerini hem de kar\u015f\u0131l\u0131k gelen beklenen \u00e7\u0131kt\u0131y\u0131 i\u00e7erir.<\/li>\n<li><strong>Deneme seti<\/strong>: Modelin g\u00f6r\u00fcnmeyen veriler \u00fczerindeki performans\u0131n\u0131 de\u011ferlendirmek i\u00e7in kullan\u0131l\u0131r. Ayr\u0131ca beklenen \u00e7\u0131kt\u0131n\u0131n yan\u0131 s\u0131ra girdi verilerini de i\u00e7erir ancak bu veriler e\u011fitim s\u00fcrecinde kullan\u0131lmaz.<\/li>\n<\/ul>\n<h3>Do\u011frulama Setleri<\/h3>\n<p>Baz\u0131 uygulamalar, model parametrelerine ince ayar yapmak i\u00e7in e\u011fitim setinden daha fazla b\u00f6l\u00fcnm\u00fc\u015f bir do\u011frulama seti de i\u00e7erir.<\/p>\n<h3>A\u015f\u0131r\u0131 Uyum ve Yetersiz Uyum<\/h3>\n<p>Verilerin uygun \u015fekilde b\u00f6l\u00fcnmesi, a\u015f\u0131r\u0131 uyumdan (bir modelin e\u011fitim verilerinde iyi performans g\u00f6sterdi\u011fi ancak g\u00f6r\u00fcnmeyen verilerde zay\u0131f performans g\u00f6sterdi\u011fi durumlarda) ve yetersiz uyumdan (modelin hem e\u011fitim hem de g\u00f6r\u00fcnmeyen verilerde k\u00f6t\u00fc performans g\u00f6sterdi\u011fi durumlarda) ka\u00e7\u0131nmaya yard\u0131mc\u0131 olur.<\/p>\n<h2>Makine \u00f6\u011frenmesinde E\u011fitim ve test setlerinin i\u00e7 yap\u0131s\u0131. Makine \u00f6\u011freniminde E\u011fitim ve test setleri nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h2>\n<p>E\u011fitim ve test k\u00fcmeleri genellikle tek bir veri k\u00fcmesinden b\u00f6l\u00fcn\u00fcr:<\/p>\n<ul>\n<li>E\u011fitim Seti: Genellikle verilerin 60-80%&#039;sini i\u00e7erir.<\/li>\n<li>Test Seti: Verinin geri kalan 20-40%&#039;sini i\u00e7erir.<\/li>\n<\/ul>\n<p>Model, e\u011fitim seti \u00fczerinde e\u011fitilir ve test seti \u00fczerinde de\u011ferlendirilerek tarafs\u0131z bir de\u011ferlendirme sa\u011flan\u0131r.<\/p>\n<h2>Makine \u00f6\u011freniminde E\u011fitim ve test setlerinin temel \u00f6zelliklerinin analizi<\/h2>\n<p>Temel \u00f6zellikler \u015funlar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>\u00d6nyarg\u0131-Varyans Dengesi<\/strong>: A\u015f\u0131r\u0131 veya eksik uyumu \u00f6nlemek i\u00e7in karma\u015f\u0131kl\u0131\u011f\u0131n dengelenmesi.<\/li>\n<li><strong>\u00c7apraz do\u011frulama<\/strong>: Farkl\u0131 veri alt k\u00fcmelerini kullanarak modelleri de\u011ferlendirme tekni\u011fi.<\/li>\n<li><strong>Genelleme<\/strong>: Modelin g\u00f6r\u00fcnmeyen veriler \u00fczerinde iyi performans g\u00f6stermesinin sa\u011flanmas\u0131.<\/li>\n<\/ul>\n<h2>Makine \u00f6\u011freniminde ne t\u00fcr E\u011fitim ve test setlerinin mevcut oldu\u011funu yaz\u0131n. Yazmak i\u00e7in tablolar\u0131 ve listeleri kullan\u0131n<\/h2>\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>Rastgele B\u00f6lme<\/td>\n<td>Verileri rastgele e\u011fitim ve test k\u00fcmelerine b\u00f6lme<\/td>\n<\/tr>\n<tr>\n<td>Katmanl\u0131 B\u00f6l\u00fcnme<\/td>\n<td>Her iki k\u00fcmede de s\u0131n\u0131flar\u0131n orant\u0131l\u0131 temsilinin sa\u011flanmas\u0131<\/td>\n<\/tr>\n<tr>\n<td>Zaman Serisi B\u00f6l\u00fcnmesi<\/td>\n<td>Zamana ba\u011fl\u0131 veriler i\u00e7in verileri kronolojik olarak b\u00f6lme<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Makine \u00f6\u011frenmesinde e\u011fitim ve test setlerinin kullan\u0131m yollar\u0131, kullan\u0131ma ili\u015fkin problemler ve \u00e7\u00f6z\u00fcmleri<\/h2>\n<p>Makine \u00f6\u011freniminde e\u011fitim ve test setlerinin kullan\u0131lmas\u0131 \u00e7e\u015fitli zorluklar\u0131 i\u00e7erir:<\/p>\n<ul>\n<li><strong>Veri s\u0131z\u0131nt\u0131s\u0131<\/strong>: Test setinden hi\u00e7bir bilginin e\u011fitim s\u00fcrecine s\u0131zmamas\u0131n\u0131 sa\u011flamak.<\/li>\n<li><strong>Dengesiz Veriler<\/strong>: Orant\u0131s\u0131z s\u0131n\u0131f temsillerine sahip veri k\u00fcmelerinin i\u015flenmesi.<\/li>\n<li><strong>Y\u00fcksek Boyutluluk<\/strong>: \u00c7ok say\u0131da \u00f6zelli\u011fe sahip verilerle ilgilenmek.<\/li>\n<\/ul>\n<p>\u00c7\u00f6z\u00fcmler aras\u0131nda dikkatli \u00f6n i\u015fleme, uygun b\u00f6lme stratejilerinin kullan\u0131lmas\u0131 ve dengesiz veriler i\u00e7in yeniden \u00f6rnekleme gibi tekniklerin kullan\u0131lmas\u0131 yer al\u0131r.<\/p>\n<h2>Tablolar ve listeler \u015feklinde ana \u00f6zellikler ve benzer terimlerle di\u011fer kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>Terim<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>E\u011fitim Seti<\/td>\n<td>Modeli e\u011fitmek i\u00e7in kullan\u0131l\u0131r<\/td>\n<\/tr>\n<tr>\n<td>Deneme seti<\/td>\n<td>Modeli de\u011ferlendirmek i\u00e7in kullan\u0131l\u0131r<\/td>\n<\/tr>\n<tr>\n<td>Do\u011frulama Seti<\/td>\n<td>Model parametrelerini ayarlamak i\u00e7in kullan\u0131l\u0131r<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Makine \u00f6\u011freniminde e\u011fitim ve test setleri ile ilgili gelece\u011fin perspektifleri ve teknolojileri<\/h2>\n<p>Bu alanda gelecekteki geli\u015fmeler \u015funlar\u0131 i\u00e7erebilir:<\/p>\n<ul>\n<li><strong>Otomatik Veri B\u00f6lme<\/strong>: Optimum veri b\u00f6l\u00fcm\u00fc i\u00e7in yapay zekadan faydalanma.<\/li>\n<li><strong>Uyarlanabilir Test<\/strong>: Modelle birlikte geli\u015fen test setlerinin olu\u015fturulmas\u0131.<\/li>\n<li><strong>Veri gizlili\u011fi<\/strong>: B\u00f6lme i\u015fleminin gizlilik k\u0131s\u0131tlamalar\u0131na uymas\u0131n\u0131n sa\u011flanmas\u0131.<\/li>\n<\/ul>\n<h2>Makine \u00f6\u011freniminde proxy sunucular nas\u0131l kullan\u0131labilir veya E\u011fitim ve test k\u00fcmeleriyle nas\u0131l ili\u015fkilendirilebilir?<\/h2>\n<p>OneProxy gibi proxy sunucular, \u00e7e\u015fitli ve co\u011frafi olarak da\u011f\u0131t\u0131lm\u0131\u015f verilere eri\u015fimi kolayla\u015ft\u0131rarak e\u011fitim ve test setlerinin \u00e7e\u015fitli ger\u00e7ek d\u00fcnya senaryolar\u0131n\u0131 temsil etmesini sa\u011flayabilir. Bu, daha sa\u011flam ve iyi genelle\u015ftirilmi\u015f modeller olu\u015fturmaya yard\u0131mc\u0131 olabilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/cross_validation.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn: E\u011fitim\/Test B\u00f6l\u00fcm\u00fc<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/tr\/\" target=\"_new\" rel=\"noopener\">OneProxy: Veri Toplaman\u0131n Geli\u015ftirilmesi<\/a><\/li>\n<li><a href=\"https:\/\/machinelearningmastery.com\" target=\"_new\" rel=\"noopener nofollow\">Makine \u00d6\u011frenimi Ustal\u0131\u011f\u0131: E\u011fitme, Do\u011frulama ve Test B\u00f6l\u00fcmlerini Anlama<\/a><\/li>\n<\/ul>","protected":false},"featured_media":470722,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479372","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Training and Test Sets in Machine Learning<\/mark>","faq_items":[{"question":"What are Training and Test Sets in Machine Learning?","answer":"<p>Training and test sets are two separate data groups used in machine learning. The training set is used to train the model, teaching it to recognize patterns and make predictions, while the test set is used to evaluate how well the model has learned and how it performs on unseen data.<\/p>"},{"question":"How Did the Concept of Training and Test Sets Originate in Machine Learning?","answer":"<p>The concept of dividing data into training and test sets emerged in the early 1970s in the field of statistical modeling. It was introduced to machine learning to avoid overfitting, ensuring that the model generalizes well on unseen data.<\/p>"},{"question":"What is the Importance of Properly Dividing Training and Test Sets?","answer":"<p>Proper division of training and test sets ensures that the model is unbiased, helping to avoid overfitting (where the model performs well on the training data but poorly on new data) and underfitting (where the model performs poorly in general).<\/p>"},{"question":"How are Training and Test Sets Structured?","answer":"<p>Typically, the training set contains 60-80% of the data, and the test set comprises the remaining 20-40%. This division allows the model to be trained on a substantial portion of the data while still being tested on unseen data to evaluate its performance.<\/p>"},{"question":"What Are Some Common Types of Training and Test Set Splits?","answer":"<p>Some common types include Random Split, where data is randomly divided; Stratified Split, ensuring proportionate class representation in both sets; and Time Series Split, where data is divided chronologically.<\/p>"},{"question":"What are the Future Perspectives Related to Training and Test Sets in Machine Learning?","answer":"<p>Future advancements may include automated data splitting using AI, adaptive testing with evolving test sets, and incorporating data privacy considerations in the splitting process.<\/p>"},{"question":"How Can Proxy Servers like OneProxy be Associated with Training and Test Sets in Machine Learning?","answer":"<p>Proxy servers such as OneProxy can provide access to diverse and geographically distributed data, ensuring that training and test sets are representative of various real-world scenarios. This aids in creating more robust and well-generalized models.<\/p>"},{"question":"What are Some Challenges and Solutions Related to the Use of Training and Test Sets in Machine Learning?","answer":"<p>Challenges include data leakage, imbalanced data, and high dimensionality. Solutions can involve careful preprocessing, proper splitting strategies, and employing techniques like resampling for imbalanced data.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/479372","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\/479372\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/470722"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=479372"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}