57 KB412 KB44 KB200420242006Curk, TomažVuk, Mihaštevilka:120 straniletnik:3str. 89-108, Graf. prikaziISSN:1854-0023ISSN:1854-0031COBISSID:5335124URN:URN:NBN:SI:doc-NL5H6AGLenFakulteta za družbene vedeMetodološki zvezkikrivulja odzivakrivulja ROCstrojno učenjeumeritvena krivuljaROC curve, lift chart and calibration plot|This paper presents ROC curve, lift chart and calibration plot, three well known graphical techniques that are useful for evaluating the quality of classification models used in data mining and machine learning. Each technique, normally used and studied separately, defines its own measure of classification quality and its visualization. Here, we give a brief survey of the methods and establish a common mathematical framework which adds some new aspects, explanations and interrelations between these techniques. We conclude with an empirical evaluation and a few examples on how to use the presented techniques to boost classification accuracyTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Ljubljani, Fakulteta za družbene vede