"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 . "1026-597X" . . . "Skalsk\u00E1, Hana" . . "2-3" . "Webov\u00E1 aplikace pro odhad AUC plochy pod k\u0159ivkou ROC"@cs . "Web-bootstrap estimate of area under ROC curve"@en . "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" . "508914" . . "[D6AE27470B83]" . "AT - Rakousk\u00E1 republika" . "35" . . . . . "discrimination; AUC estimate; resampling"@en . "325-330" . . . "18450" . "6"^^ . . . "Austrian Journal of Statistics" . "Web-bootstrap estimate of area under ROC curve"@en . . . . "Webov\u00E1 aplikace pro odhad AUC plochy pod k\u0159ivkou ROC"@cs . "Web-bootstrap estimate of area under ROC curve" . "RIV/62690094:18450/06:00002015" . "P(GA402/04/1308), S" . "RIV/62690094:18450/06:00002015!RIV08-MSM-18450___" . "2"^^ . "Web-bootstrap estimate of area under ROC curve" . . "Freylich, V\u00E1clav" . . "2"^^ . "P\u0159esnost bin\u00E1rn\u00EDho klasifika\u010Dn\u00EDho pravidla (tj. rozli\u0161en\u00ED skupiny bez ur\u010Dit\u00E9 vlastnosti od skupiny s ur\u010Ditou vlastnost\u00ED) je zpravidla posuzov\u00E1na pomoc\u00ED klasifika\u010Dn\u00ED matice. K\u0159ivka ROC umo\u017E\u0148uje visualizaci vztahu mezi pravd\u011Bpodobnost\u00ED nespr\u00E1vn\u00E9 klasifikace prvk\u016F ze skupiny s ur\u010Ditou danou vlastnost\u00ED (pozitivn\u00EDch prvk\u016F) a mezi pravd\u011Bpodobnost\u00ED spr\u00E1vn\u00E9 klasifikace ve skupin\u011B bez dan\u00E9 vlastnosti (prvk\u016F negativn\u00EDch), a to pro v\u0161echny hodnoty diskrimina\u010Dn\u00EDho sk\u00F3re. AUC, plocha pod ROC k\u0159ivkou, je jednou ze souhrnn\u00FDch m\u011Br. \u010Cl\u00E1nek popisuje mo\u017Enost odhadu AUC pomoc\u00ED webov\u00E9 aplikace, kter\u00E1 je zalo\u017Eena na odhadu pomoc\u00ED metody bootstrap. Bootstrap je metoda vhodn\u00E1 zejm\u00E9na v p\u0159\u00EDpadech, kdy\u017E nen\u00ED d\u00E1n p\u0159edpoklad o tvaru rozd\u011Blen\u00ED dikrimina\u010Dn\u00EDho sk\u00F3re v obou skupin\u00E1ch. Kvalita uveden\u00E9 aplikace byla testov\u00E1na pomoc\u00ED speci\u00E1ln\u00EDho programu kter\u00FD je naps\u00E1n v C#.NET. Tento program umo\u017Enil prov\u00E9st opakovan\u00E9 testy bootstrap aplikace a vyhodnotit jej\u00ED vlastnosti. Odhady AUC a jejich meze spolehlivosti byly d\u00E1le porovn\u00E1ny"@cs .