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Description
  • Usually predictive quality of discrimination model for separation two classes is evaluated by apparent error rate given from confusion matrix. Receiver operating characteristic (ROC) describes and visualises information from confusion matrices given across all possible decision thresholds of classification model. Area under ROC curve (AUC) can be used as a summary measure. This article describes the application of bootstrap (resampling) to data for which the binormal distribution assumption is not appropriate. Different estimates of AUC and their confidence limits are compared. No substantial differences between estimates were found. Results can indicate the small advantage of bootstrap in determination of confidence intervals for small data samples and very skewed distributions
  • Usually predictive quality of discrimination model for separation two classes is evaluated by apparent error rate given from confusion matrix. Receiver operating characteristic (ROC) describes and visualises information from confusion matrices given across all possible decision thresholds of classification model. Area under ROC curve (AUC) can be used as a summary measure. This article describes the application of bootstrap (resampling) to data for which the binormal distribution assumption is not appropriate. Different estimates of AUC and their confidence limits are compared. No substantial differences between estimates were found. Results can indicate the small advantage of bootstrap in determination of confidence intervals for small data samples and very skewed distributions (en)
  • Kvalita predikce diskriminačního modelu pro oddělení dvou tříd je obvykle hodnocena pomocí chyby z tzv konfuzní matice. Receiver operating characteristic (ROC) popisuje a vizualizuje informaci z konfuzní matice dané přes všechny možné rozhodovací prahy klasifikačního modelu. Plocha pod ROC křivkou (AUC) může být použita jako souhrnná míra. Článek ukazuje použití metody bootstrap na data, pro která není adekvátní binormální rozdělení. Jsou porovnány různé odhady AUC a jejich spolehlivost. Nebyl nalezen podstatný rozdíl mezi odhady. Výsledky mohou indikovat malou výhodu metody bootstrap při určení intervalu spolehlivosti pro malé vzorky dat a velmi šikmá rozdělení. (cs)
Title
  • Estimate of AUC by Resampling.
  • Estimate of AUC by Resampling. (en)
  • Odhad AUC metodou bootstrap (cs)
skos:prefLabel
  • Estimate of AUC by Resampling.
  • Estimate of AUC by Resampling. (en)
  • Odhad AUC metodou bootstrap (cs)
skos:notation
  • RIV/62690094:18450/04:00001340!RIV06-GA0-18450___
http://linked.open.../vavai/riv/strany
  • 288-291
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA402/04/1308)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 563156
http://linked.open...ai/riv/idVysledku
  • RIV/62690094:18450/04:00001340
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • discrimination; AUC estimate; resampling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [A81F1338F44C]
http://linked.open...v/mistoKonaniAkce
  • Brno
http://linked.open...i/riv/mistoVydani
  • Brno
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 22nd International Conference Mathematical Methods in Economics
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Skalská, Hana
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • Masarykova univerzita
https://schema.org/isbn
  • 80-210-3496-3
http://localhost/t...ganizacniJednotka
  • 18450
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