Original language | English |
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Title of host publication | Encyclopedia of Machine Learning and Data Mining |

Editors | Claude Sammut, Geoffrey I Webb |

Publisher | Springer |

Pages | 1-8 |

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.

### Structured keywords

- Jean Golding

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## Cite this

Flach, P. A. (2016). ROC Analysis. In C. Sammut, & G. I. Webb (Eds.),

*Encyclopedia of Machine Learning and Data Mining*(pp. 1-8). Springer. https://doi.org/10.1007/978-1-4899-7502-7_739-1