auc: Area under the ROC curve (AUC)

In Metrics: Evaluation Metrics for Machine Learning

Description

auc computes the area under the receiver-operator characteristic curve (AUC).

Usage

auc(actual, predicted)

Arguments

actual	The ground truth binary numeric vector containing 1 for the positive class and 0 for the negative class.
predicted	A numeric vector of predicted values, where the smallest values correspond to the observations most believed to be in the negative class and the largest values indicate the observations most believed to be in the positive class. Each element represents the prediction for the corresponding element in actual.

Details

auc uses the fact that the area under the ROC curve is equal to the probability that a randomly chosen positive observation has a higher predicted value than a randomly chosen negative value. In order to compute this probability, we can calculate the Mann-Whitney U statistic. This method is very fast, since we do not need to compute the ROC curve first.

Examples

actual <- c(1, 1, 1, 0, 0, 0)
predicted <- c(0.9, 0.8, 0.4, 0.5, 0.3, 0.2)
auc(actual, predicted)

Example output

[1] 0.8888889

Metrics documentation built on May 1, 2019, 10:11 p.m.