Resampling ROC curves
نویسندگان
چکیده
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 estimation corresponds to the case where one applies a strict decision rule. But in many other applications one just uses a “score” S as a rating of the risk to be a member of one group, and any monotonic increasing transformation of S is also a score. Usual scores are obtained with linear classifiers (Fisher’s discriminant analysis, logistic regression) but since the probability is also a score ranging from
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