ROC Analysis Task

Example(s):
- an AUC Analysis, such as in File:AUC.150719.xlsx.
- …
Counter-Example(s):
- Receiver Operating Characteristic.
See: Analysis Task, Accuracy Measure; Class Imbalance Problem; Classification Task; Confusion Matrix; Cost-Sensitive Learning; Error Rate; False Negative Rate; False Positive Rate; True Negative Rate; True Positive Rate, Gaussian Distribution; Posterior Probability; Precision; Prior Probability.

References

(Huang et al., 2012) ⇒ Xin Huang, Gengsheng Qin, Yan Yuan, and Xiao-hua Zhou. (2012). “Confidence Intervals for the Difference Between Two Partial AUCs.” In: Australian \& New Zealand Journal of Statistics, 54(1). doi:10.1111/j.1467-842X.2012.00648.x

(Flach, 2011b) ⇒ Peter A. Flach. (2011). “ROC Analysis.” In: (Sammut & Webb, 2011) p.869
- QUOTE: 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). ...