منابع مشابه
Technical Note: Towards ROC Curves in Cost Space
ROC curves and cost curves are two popular ways of visualising classifier performance, finding appropriate thresholds according to the operating condition, and deriving useful aggregated measures such as the area under the ROC curve (AUC) or the area under the optimal cost curve. In this note we present some new findings and connections between ROC space and cost space, by using the expected lo...
متن کاملWhat ROC Curves Can't Do (and Cost Curves Can)
This paper shows that ROC curves, as a method of visualizing classifier performance, are inadequate for the needs of Artificial Intelligence researchers in several significant respects, and demonstrates that a different way of visualizing performance – the cost curves introduced by Drummond and Holte at KDD’2000 – overcomes these deficiencies.
متن کاملRepairing Concavities in ROC Curves
Declaration This dissertation is submitted to the University of Bristol in accordance with the requirements of the degree of Bachelor of Science in the Faculty of Engineering. It has not been submitted for any other degree or diploma of any examining body. Except where specifically acknowledged, it is all the work of the Author. 3 ABSTRACT Machine Learning applications require learning algorith...
متن کاملROC Curves in medical decision
The accurate medical diagnostic of a disease condition is fundamental for a correct medical decision. Disease screening programs are based, in general, in diagnostic tests which provide a binary response: a subject is classified as positive, if the test result is above a given threshold, and negative, otherwise. Therefore, false positive and false negative classifications can be generated. The ...
متن کاملResampling ROC curves
Receiver operating characteristic (ROC) curves are very popular for evaluating a diagnostic test or score performances in various decision making applications: medicine, marketing, credit scoring etc. The ROC curve provides a concise graphical representation of the trade off between sensitivity and specificity. We will focus here on supervised classification into two groups. Error rate estimati...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2013
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-013-5328-9