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Description
  • Křivka ROC (Receiver Operating Characteristic) a plocha pod touto křivkou AUC (Area Under the Curve) jsou metody vhodné a často využívané pro vizualizaci a kvantifikaci prediktivních vlastností binárního klasifikačního modelu. Tento článek se zabývá možnostmi využití dostupného softwaru pro tyto odhady. Jsou porovnávány statistické systémy programů NCSS, SPSS a MedCalc s programem freewarovým ROCkit. Jsou zmíněny rovněž vlastnosti dalších programových systémů (R, Matlab a webové aplikace). Jsou prezentovány a porovnávány s výše zmíněným softwarem také výsledky simulačního experimentu, které poskytuje vlastní webová aplikace odhadu, která je založena na metodě bootstrap. Výsledky simulačního experimentu indikují nevychýlený bootstrap odhad AUC a užší meze spolehlivosti v porovnání s ostatními metodami odhadu, dostupnými v porovnávaných softwarových implementacích. (cs)
  • Receiver Operating Characteristic (ROC) Curve and the Area under the Curve (AUC) are the methods which are frequently used now for visualisation and quantification of the predictive ability of binary classification model. This article reports about the possibilities of some software for AUC estimates. Statistical packages NCSS, SPSS, and MedCalc are compared with the most complete freeware ROCkit. Other possibilities (R, Matlab and web based applications) are mentioned as well. Finally some results of simulation experiments based on bootstrap application are presented. Results of simulation experiment with bootstrap estimates indicated unbiased estimates of AUC and the narrowest confidence limits.
  • Receiver Operating Characteristic (ROC) Curve and the Area under the Curve (AUC) are the methods which are frequently used now for visualisation and quantification of the predictive ability of binary classification model. This article reports about the possibilities of some software for AUC estimates. Statistical packages NCSS, SPSS, and MedCalc are compared with the most complete freeware ROCkit. Other possibilities (R, Matlab and web based applications) are mentioned as well. Finally some results of simulation experiments based on bootstrap application are presented. Results of simulation experiment with bootstrap estimates indicated unbiased estimates of AUC and the narrowest confidence limits. (en)
Title
  • A Comparison of Software for ROC and AUC Estimates
  • A Comparison of Software for ROC and AUC Estimates (en)
  • Porovnání software pro odhady ROC a AUC (cs)
skos:prefLabel
  • A Comparison of Software for ROC and AUC Estimates
  • A Comparison of Software for ROC and AUC Estimates (en)
  • Porovnání software pro odhady ROC a AUC (cs)
skos:notation
  • RIV/62690094:18450/07:00001864!RIV07-GA0-18450___
http://linked.open.../vavai/riv/strany
  • 449-453
http://linked.open...avai/riv/aktivita
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  • P(GA402/04/1308)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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  • 407837
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  • RIV/62690094:18450/07:00001864
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  • AUC; ROC Curve; classification; software (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D604722FFEEB]
http://linked.open...i/riv/mistoVydani
  • Bratislava
http://linked.open...i/riv/nazevZdroje
  • Aplimat 2007, 6th International Conference Proceedings
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
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http://linked.open...iv/tvurceVysledku
  • Skalská, Hana
number of pages
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  • Slovenská technická univerzita v Bratislave
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  • 978-80-969562-4-1
http://localhost/t...ganizacniJednotka
  • 18450
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