Original language  English 

Title of host publication  Encyclopedia of Machine Learning and Data Mining 
Editors  Claude Sammut, Geoffrey I Webb 
Publisher  Springer 
Pages  18 
Number of pages  8 
ISBN (Electronic)  9781489975027 
DOIs  
Publication status  Published  7 Oct 2016 
Abstract
ROC analysis investigates and employs the relationship between sensitivity and specificity of a binary classifier. Sensitivity or true positiverate measures the proportion of positives correctly classified; specificity or true negativerate measures the proportion of negatives correctly classified. Conventionally, the true positive rate tpr is plotted against the false positiverate 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 ROC analysis. It can be used to address a range of problems, including: (1) determining a decision threshold that minimises error rate or misclassification cost under given class and cost distributions; (2) identifying regions where one classifier outperforms another; (3) identifying regions where a classifier performs worse than chance; and (4) obtaining calibrated estimates of the class posterior.
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 Jean Golding
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ROC Analysis
Flach, P. A., 2010, Encyclopedia of Machine Learning and Data Mining. Springer, p. 869875 7 p.Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary