Area Under the Curve
Compute true positive rates, false positive rates and the area under the curve to evaulate the algorihtms performance. Efficient implementation according to
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. Link
Functions
MultivariateAnomalies.auc — Functionauc(scores, events, increasing = true)compute the Area Under the receiver operator Curve (AUC), given some output scores array and some ground truth (events). By default, it is assumed, that the scores are ordered increasingly (increasing = true), i.e. high scores represent events. AUC is computed according to Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. http://doi.org/10.1016/j.patrec.2005.10.010
Examples
julia> scores = rand(10, 2)
julia> events = rand(0:1, 10, 2)
julia> auc(scores, events)
julia> auc(scores, boolevents(events))Index