ROC Analysis

Peter A Flach

doi:10.1007/978-0-387-30164-8_733

ROC Analysis

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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 contribution	ROC Analysis
Original language	English
Title of host publication	Encyclopedia of Machine Learning and Data Mining
Publisher	Springer
Pages	869-875
Number of pages	7
ISBN (Electronic)	9780387301648
ISBN (Print)	9780387307688
DOIs	https://doi.org/10.1007/978-0-387-30164-8_733
Publication status	Published - 2010

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Other identifier: 2001393

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ROC Analysis
Flach, P. A., 7 Oct 2016, Encyclopedia of Machine Learning and Data Mining. Sammut, C. & Webb, G. I. (eds.). Springer, p. 1-8 8 p.
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title = "ROC Analysis",

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 ...",

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AB - 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 ...

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DO - 10.1007/978-0-387-30164-8_733

M3 - Entry for encyclopedia/dictionary

SN - 9780387307688

SP - 869

EP - 875

BT - Encyclopedia of Machine Learning and Data Mining

PB - Springer

ER -

ROC Analysis

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ROC Analysis

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