{"id":476400,"date":"2023-08-09T07:29:55","date_gmt":"2023-08-09T07:29:55","guid":{"rendered":""},"modified":"2023-09-05T11:12:41","modified_gmt":"2023-09-05T11:12:41","slug":"confusion-matrix","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/confusion-matrix\/","title":{"rendered":"Kar\u0131\u015f\u0131kl\u0131k matrisi"},"content":{"rendered":"<p>Kar\u0131\u015f\u0131kl\u0131k Matrisi, makine \u00f6\u011frenimi ve yapay zeka modellerinin de\u011ferlendirilmesi i\u00e7in \u00f6nemli bir ara\u00e7t\u0131r ve performanslar\u0131na ili\u015fkin kritik bilgiler sa\u011flar. Bu performans, s\u0131n\u0131fland\u0131rma problemlerinde \u00e7e\u015fitli veri s\u0131n\u0131flar\u0131 genelinde \u00f6l\u00e7\u00fcl\u00fcr.<\/p>\n<h2>Kar\u0131\u015f\u0131kl\u0131k Matrisinin Tarihi ve K\u00f6keni<\/h2>\n<p>Kar\u0131\u015f\u0131kl\u0131k Matrisi i\u00e7in tan\u0131mlanm\u0131\u015f tek bir ba\u015flang\u0131\u00e7 noktas\u0131 olmasa da ilkeleri, \u0130kinci D\u00fcnya Sava\u015f\u0131&#039;ndan bu yana sinyal tespit teorisinde \u00f6rt\u00fcl\u00fc olarak kullan\u0131lmaktad\u0131r. \u00d6ncelikle g\u00fcr\u00fclt\u00fcn\u00fcn ortas\u0131nda sinyallerin varl\u0131\u011f\u0131n\u0131 ay\u0131rt etmek i\u00e7in kullan\u0131ld\u0131. Ancak \u201cKar\u0131\u015f\u0131kl\u0131k Matrisi\u201d teriminin \u00f6zellikle makine \u00f6\u011frenimi ve veri bilimi ba\u011flam\u0131nda modern kullan\u0131m\u0131, bu alanlar\u0131n y\u00fckseli\u015fiyle birlikte 20. y\u00fczy\u0131l\u0131n sonlar\u0131nda pop\u00fclerlik kazanmaya ba\u015flad\u0131.<\/p>\n<h2>Kar\u0131\u015f\u0131kl\u0131k Matrisine Derinlemesine Bir Bak\u0131\u015f<\/h2>\n<p>Kar\u0131\u015f\u0131kl\u0131k Matrisi, esas olarak bir algoritman\u0131n (genellikle denetimli \u00f6\u011frenme algoritmas\u0131) performans\u0131n\u0131n g\u00f6rselle\u015ftirilmesine olanak tan\u0131yan bir tablo d\u00fczenidir. Precision, Recall, F-Score ve deste\u011fin \u00f6l\u00e7\u00fclmesinde olduk\u00e7a faydal\u0131d\u0131r. Matristeki her sat\u0131r, ger\u00e7ek s\u0131n\u0131f\u0131n \u00f6rneklerini temsil ederken, her s\u00fctun, tahmin edilen s\u0131n\u0131f\u0131n \u00f6rneklerini belirtir veya bunun tersi de ge\u00e7erlidir.<\/p>\n<p>Matrisin kendisi d\u00f6rt ana bile\u015fen i\u00e7erir: Ger\u00e7ek Pozitifler (TP), Ger\u00e7ek Negatifler (TN), Yanl\u0131\u015f Pozitifler (FP) ve Yanl\u0131\u015f Negatifler (FN). Bu bile\u015fenler bir s\u0131n\u0131fland\u0131rma modelinin temel performans\u0131n\u0131 tan\u0131mlar.<\/p>\n<ul>\n<li>Ger\u00e7ek Pozitifler: Bu, model taraf\u0131ndan do\u011fru \u015fekilde s\u0131n\u0131fland\u0131r\u0131lan pozitif \u00f6rneklerin say\u0131s\u0131n\u0131 temsil eder.<\/li>\n<li>Ger\u00e7ek Negatifler: Bu, model taraf\u0131ndan do\u011fru \u015fekilde s\u0131n\u0131fland\u0131r\u0131lan negatif \u00f6rneklerin say\u0131s\u0131n\u0131 g\u00f6sterir.<\/li>\n<li>Yanl\u0131\u015f Pozitifler: Bunlar model taraf\u0131ndan yanl\u0131\u015f \u015fekilde s\u0131n\u0131fland\u0131r\u0131lan pozitif \u00f6rneklerdir.<\/li>\n<li>Yanl\u0131\u015f Negatifler: Bunlar, model taraf\u0131ndan yanl\u0131\u015f \u015fekilde s\u0131n\u0131fland\u0131r\u0131lan negatif \u00f6rnekleri temsil eder.<\/li>\n<\/ul>\n<h2>Kar\u0131\u015f\u0131kl\u0131k Matrisinin \u0130\u00e7 Yap\u0131s\u0131 ve \u0130\u015fleyi\u015fi<\/h2>\n<p>Kar\u0131\u015f\u0131kl\u0131k Matrisi, ger\u00e7ek ve \u00f6ng\u00f6r\u00fclen sonu\u00e7lar\u0131 kar\u015f\u0131la\u015ft\u0131rarak \u00e7al\u0131\u015f\u0131r. \u0130kili s\u0131n\u0131fland\u0131rma probleminde a\u015fa\u011f\u0131daki format\u0131 al\u0131r:<\/p>\n<table>\n<thead>\n<tr>\n<th><\/th>\n<th>Olumlu Tahmin Edildi<\/th>\n<th>Negatif Tahmin Edildi<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Ger\u00e7ek Olumlu<\/td>\n<td>TP<\/td>\n<td>FN<\/td>\n<\/tr>\n<tr>\n<td>Ger\u00e7ek Negatif<\/td>\n<td>FP<\/td>\n<td>TN<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Matris bile\u015fenleri daha sonra do\u011fruluk, kesinlik, hat\u0131rlama ve F1 puan\u0131 gibi \u00f6nemli \u00f6l\u00e7\u00fcmleri hesaplamak i\u00e7in kullan\u0131l\u0131r.<\/p>\n<h2>Kar\u0131\u015f\u0131kl\u0131k Matrisinin Temel \u00d6zellikleri<\/h2>\n<p>A\u015fa\u011f\u0131daki \u00f6zellikler Kar\u0131\u015f\u0131kl\u0131k Matrisine \u00f6zg\u00fcd\u00fcr:<\/p>\n<ol>\n<li><strong>\u00c7ok Boyutlu \u0130\u00e7g\u00f6r\u00fc:<\/strong> Tek bir do\u011fruluk puan\u0131 yerine modelin performans\u0131na ili\u015fkin \u00e7ok boyutlu bir g\u00f6r\u00fcn\u00fcm sa\u011flar.<\/li>\n<li><strong>Tan\u0131mlama hatas\u0131:<\/strong> \u0130ki t\u00fcr hatan\u0131n (yanl\u0131\u015f pozitifler ve yanl\u0131\u015f negatifler) tan\u0131mlanmas\u0131n\u0131 sa\u011flar.<\/li>\n<li><strong>\u00d6nyarg\u0131 Tan\u0131mlamas\u0131:<\/strong> Belirli bir s\u0131n\u0131fa y\u00f6nelik bir tahmin \u00f6nyarg\u0131s\u0131 olup olmad\u0131\u011f\u0131n\u0131 belirlemeye yard\u0131mc\u0131 olur.<\/li>\n<li><strong>Performans Metrikleri:<\/strong> \u00c7oklu performans \u00f6l\u00e7\u00fcmlerinin hesaplanmas\u0131na yard\u0131mc\u0131 olur.<\/li>\n<\/ol>\n<h2>Kar\u0131\u015f\u0131kl\u0131k Matrisi T\u00fcrleri<\/h2>\n<p>Esasen tek bir Kar\u0131\u015f\u0131kl\u0131k Matrisi t\u00fcr\u00fc olmas\u0131na ra\u011fmen, problem alan\u0131nda s\u0131n\u0131fland\u0131r\u0131lacak s\u0131n\u0131flar\u0131n say\u0131s\u0131, matrisi daha fazla boyuta geni\u015fletebilir. \u0130kili s\u0131n\u0131fland\u0131rma i\u00e7in matris 2\u00d72&#039;dir. &#039;n&#039; s\u0131n\u0131fl\u0131 \u00e7ok s\u0131n\u0131fl\u0131 bir problem i\u00e7in bu bir &#039;nxn&#039; matrisi olacakt\u0131r.<\/p>\n<h2>Kullan\u0131mlar, Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h2>\n<p>Kar\u0131\u015f\u0131kl\u0131k Matrisi \u00f6ncelikle makine \u00f6\u011frenimi ve yapay zekadaki s\u0131n\u0131fland\u0131rma modellerini de\u011ferlendirmek i\u00e7in kullan\u0131l\u0131r. Ancak zorluklar\u0131 da yok de\u011fil. \u00d6nemli sorunlardan biri, dengesiz veri k\u00fcmeleri durumunda matristen elde edilen do\u011frulu\u011fun yan\u0131lt\u0131c\u0131 olabilmesidir. Burada Hassasiyet-Geri \u00c7a\u011f\u0131rma e\u011frileri veya E\u011fri Alt\u0131ndaki Alan (AUC-ROC) daha uygun olabilir.<\/p>\n<h2>Benzer Terimlerle Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>Metrikler<\/th>\n<th>Elde edilen<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Kesinlik<\/td>\n<td>Kar\u0131\u015f\u0131kl\u0131k Matrisi<\/td>\n<td>Modelin genel do\u011frulu\u011funu \u00f6l\u00e7er<\/td>\n<\/tr>\n<tr>\n<td>Kesinlik<\/td>\n<td>Kar\u0131\u015f\u0131kl\u0131k Matrisi<\/td>\n<td>Yaln\u0131zca olumlu tahminlerin do\u011frulu\u011funu \u00f6l\u00e7er<\/td>\n<\/tr>\n<tr>\n<td>Geri \u00c7a\u011f\u0131rma (Hassasiyet)<\/td>\n<td>Kar\u0131\u015f\u0131kl\u0131k Matrisi<\/td>\n<td>Modelin t\u00fcm pozitif \u00f6rnekleri bulma yetene\u011fini \u00f6l\u00e7er<\/td>\n<\/tr>\n<tr>\n<td>F1 Puan\u0131<\/td>\n<td>Kar\u0131\u015f\u0131kl\u0131k Matrisi<\/td>\n<td>Hassasiyet ve Geri \u00c7a\u011f\u0131rman\u0131n Harmonik Ortalamas\u0131<\/td>\n<\/tr>\n<tr>\n<td>\u00f6zg\u00fcll\u00fck<\/td>\n<td>Kar\u0131\u015f\u0131kl\u0131k Matrisi<\/td>\n<td>Modelin t\u00fcm negatif \u00f6rnekleri bulma yetene\u011fini \u00f6l\u00e7er<\/td>\n<\/tr>\n<tr>\n<td>AUC-ROC<\/td>\n<td>ROC E\u011frisi<\/td>\n<td>Duyarl\u0131l\u0131k ve \u00d6zg\u00fcll\u00fck aras\u0131ndaki dengeyi g\u00f6sterir<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Gelecek Perspektifleri ve Teknolojiler<\/h2>\n<p>Yapay zeka ve makine \u00f6\u011freniminin devam eden geli\u015fimiyle birlikte, Kar\u0131\u015f\u0131kl\u0131k Matrisinin model de\u011ferlendirmede \u00f6nemli bir ara\u00e7 olarak kalmas\u0131 bekleniyor. \u0130yile\u015ftirmeler, daha iyi g\u00f6rselle\u015ftirme tekniklerini, i\u00e7g\u00f6r\u00fc elde etmede otomasyonu ve daha geni\u015f bir makine \u00f6\u011frenimi g\u00f6rev yelpazesinde uygulamay\u0131 i\u00e7erebilir.<\/p>\n<h2>Proxy Sunucular\u0131 ve Kar\u0131\u015f\u0131kl\u0131k Matrisi<\/h2>\n<p>OneProxy taraf\u0131ndan sa\u011flananlar gibi proxy sunucular, genellikle makine \u00f6\u011frenimi g\u00f6revlerinin \u00f6nc\u00fcs\u00fc olan sorunsuz, g\u00fcvenli ve anonim web kaz\u0131ma ve veri madencili\u011fi i\u015flemlerinin sa\u011flanmas\u0131nda hayati bir rol oynar. Toplanan veriler daha sonra model e\u011fitimi ve Kar\u0131\u015f\u0131kl\u0131k Matrisi kullan\u0131larak sonraki de\u011ferlendirme i\u00e7in kullan\u0131labilir.<\/p>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<p>Kar\u0131\u015f\u0131kl\u0131k Matrisi hakk\u0131nda daha fazla bilgi edinmek i\u00e7in a\u015fa\u011f\u0131daki kaynaklar\u0131 g\u00f6z \u00f6n\u00fcnde bulundurun:<\/p>\n<ol>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Confusion_matrix\" target=\"_new\" rel=\"noopener nofollow\">Kar\u0131\u015f\u0131kl\u0131k Matrisi hakk\u0131ndaki Wikipedia makalesi<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/understanding-confusion-matrix-a9ad42dcfd62\" target=\"_new\" rel=\"noopener nofollow\">Veri Bilimine Do\u011fru: Kar\u0131\u015f\u0131kl\u0131k Matrisini Anlamak<\/a><\/li>\n<li><a href=\"https:\/\/www.datacamp.com\/community\/tutorials\/understanding-confusion-matrices\" target=\"_new\" rel=\"noopener nofollow\">DataCamp&#039;\u0131n Python&#039;daki Kar\u0131\u015f\u0131kl\u0131k Matrisi hakk\u0131ndaki \u00f6\u011freticisi<\/a><\/li>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.metrics.confusion_matrix.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-learn&#039;in Kar\u0131\u015f\u0131kl\u0131k Matrisine ili\u015fkin belgeleri<\/a><\/li>\n<\/ol>","protected":false},"featured_media":467991,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476400","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Understanding the Confusion Matrix: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is a Confusion Matrix?","answer":"<p>A Confusion Matrix is a performance measurement tool for machine learning classification problems. It provides a visualization of the performance of an algorithm, measuring precision, recall, F-score, and support. It consists of four components - True Positives, True Negatives, False Positives, and False Negatives - that represent the basic performance of a classification model.<\/p>"},{"question":"What is the history of the Confusion Matrix?","answer":"<p>The principles of the Confusion Matrix have been used implicitly in signal detection theory since World War II. Its modern use, particularly in machine learning and data science, began to gain popularity in the late 20th century.<\/p>"},{"question":"How does the Confusion Matrix work?","answer":"<p>The Confusion Matrix works by comparing the actual and predicted outcomes of a classification problem. Each row of the matrix represents instances of the actual class, while each column signifies instances of the predicted class, or vice versa.<\/p>"},{"question":"What are the key features of the Confusion Matrix?","answer":"<p>The key features of the Confusion Matrix include providing multi-dimensional insight into a model's performance, identifying types of errors\u2014false positives and false negatives\u2014, detecting if there is a prediction bias towards a particular class, and assisting in the calculation of multiple performance metrics.<\/p>"},{"question":"What types of Confusion Matrix exist?","answer":"<p>While there's essentially one type of Confusion Matrix, its dimensions can vary based on the number of classes to be classified in the problem domain. For binary classification, the matrix is 2x2. For a multiclass problem with 'n' classes, it would be an 'nxn' matrix.<\/p>"},{"question":"What are the uses and potential problems of the Confusion Matrix?","answer":"<p>The Confusion Matrix is used to evaluate classification models in machine learning and AI. However, it may provide misleading accuracy in the case of imbalanced datasets. In such cases, other metrics such as Precision-Recall curves or the Area Under the Curve (AUC-ROC) might be more appropriate.<\/p>"},{"question":"What is the connection between proxy servers and the Confusion Matrix?","answer":"<p>Proxy servers like those provided by OneProxy are integral to web scraping and data mining operations, which are often precursors to machine learning tasks. The data scraped can then be used for model training and subsequent evaluation using the Confusion Matrix.<\/p>"},{"question":"Where can I learn more about the Confusion Matrix?","answer":"<p>You can learn more about the Confusion Matrix from various resources, including the Wikipedia article on Confusion Matrix, the 'Towards Data Science' blog on understanding Confusion Matrix, DataCamp's tutorial on Confusion Matrix in Python, and Scikit-learn's documentation on Confusion Matrix.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476400","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\/476400\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/467991"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=476400"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}