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| rdf:type
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| Description
| - The accuracy of binary discrimination models (discrimination between cases with and without any condition) is usually summarized by classification matrix (also called a confusion, assignment, or prediction matrix). Receiver operating characteristic (ROC) curve can visualize the association between probabilities of incorrect classification of cases from the group without condition (False Positives) versus the probabilities of correct classification of cases from the group with condition (True Positives) across all the possible cut-point values of discrimination score. Area under ROC curve (AUC) is one of summary measures. This article describes the possibility of AUC estimate with the use of web based application of bootstrap (re-sampling). Bootstrap is useful mainly to data for which any distributional assumptions are not appropriate. The quality of the bootstrap application was evaluated with the use of a special programme written in $C\sharp .NET$ language that allows to automate t
- The accuracy of binary discrimination models (discrimination between cases with and without any condition) is usually summarized by classification matrix (also called a confusion, assignment, or prediction matrix). Receiver operating characteristic (ROC) curve can visualize the association between probabilities of incorrect classification of cases from the group without condition (False Positives) versus the probabilities of correct classification of cases from the group with condition (True Positives) across all the possible cut-point values of discrimination score. Area under ROC curve (AUC) is one of summary measures. This article describes the possibility of AUC estimate with the use of web based application of bootstrap (re-sampling). Bootstrap is useful mainly to data for which any distributional assumptions are not appropriate. The quality of the bootstrap application was evaluated with the use of a special programme written in $C\sharp .NET$ language that allows to automate t (en)
- Přesnost binárního klasifikačního pravidla (tj. rozlišení skupiny bez určité vlastnosti od skupiny s určitou vlastností) je zpravidla posuzována pomocí klasifikační matice. Křivka ROC umožňuje visualizaci vztahu mezi pravděpodobností nesprávné klasifikace prvků ze skupiny s určitou danou vlastností (pozitivních prvků) a mezi pravděpodobností správné klasifikace ve skupině bez dané vlastnosti (prvků negativních), a to pro všechny hodnoty diskriminačního skóre. AUC, plocha pod ROC křivkou, je jednou ze souhrnných měr. Článek popisuje možnost odhadu AUC pomocí webové aplikace, která je založena na odhadu pomocí metody bootstrap. Bootstrap je metoda vhodná zejména v případech, když není dán předpoklad o tvaru rozdělení dikriminačního skóre v obou skupinách. Kvalita uvedené aplikace byla testována pomocí speciálního programu který je napsán v C#.NET. Tento program umožnil provést opakované testy bootstrap aplikace a vyhodnotit její vlastnosti. Odhady AUC a jejich meze spolehlivosti byly dále porovnány (cs)
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| Title
| - Web-bootstrap estimate of area under ROC curve
- Webová aplikace pro odhad AUC plochy pod křivkou ROC (cs)
- Web-bootstrap estimate of area under ROC curve (en)
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| skos:prefLabel
| - Web-bootstrap estimate of area under ROC curve
- Webová aplikace pro odhad AUC plochy pod křivkou ROC (cs)
- Web-bootstrap estimate of area under ROC curve (en)
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| skos:notation
| - RIV/62690094:18450/06:00002015!RIV08-MSM-18450___
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| http://linked.open.../vavai/riv/strany
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/62690094:18450/06:00002015
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - discrimination; AUC estimate; resampling (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
| - Austrian Journal of Statistics
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
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| http://linked.open...iv/tvurceVysledku
| - Skalská, Hana
- Freylich, Václav
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| issn
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| number of pages
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| http://localhost/t...ganizacniJednotka
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| is http://linked.open...avai/riv/vysledek
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