ROC Analysis

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary

Abstract

ROC analysis investigates and employs the relationship between sensitivity and specificity of a binary classifier. Sensitivity or true positive rate measures the proportion of positives correctly classified; specificity or true negative rate measures the proportion of negatives correctly classified. Conventionally, the true positive rate (tpr) is plotted against the false positive rate (fpr), which is one minus true negative rate. If a classifier outputs a score proportional to its belief that an instance belongs to the positive class, decreasing the decision threshold – above which an instance is deemed to belong to the positive class – will increase both true and false positive rates. Varying the decision threshold from its maximal to its minimal value results in a piecewise linear curve from (0, 0) to (1, 1), such that each segment has a nonnegative slope (Fig. 1). This ROC curve is the main tool used in R ...
Translated title of the contributionROC Analysis
Original languageEnglish
Title of host publicationEncyclopedia of Machine Learning and Data Mining
PublisherSpringer
Pages869-875
Number of pages7
ISBN (Electronic)9780387301648
ISBN (Print)9780387307688
DOIs
Publication statusPublished - 2010

Bibliographical note

Other identifier: 2001393

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