Finds the area under the ROC curve (a curve with sensitivity on the Y-axis
and 1 - specificity on the X-axis). This is a useful metric for
determining how well a test statistic discriminates between two classes.
The following assumptions are made in this implementation:

1. For some cutoff value c and test statistic T, your decision rule is of
the form "Class A if T > c, Class B if T < c".

2. In the case of ties, i.e. if class A and class B both have an identical
value, linear interpolation is used. This is because changing the
value of c infinitesimally will change both sensitivity and specificity
in these cases.

Finds the area under the ROC curve (a curve with sensitivity on the Y-axis and 1 - specificity on the X-axis). This is a useful metric for determining how well a test statistic discriminates between two classes. The following assumptions are made in this implementation:

1. For some cutoff value c and test statistic T, your decision rule is of the form "Class A if T > c, Class B if T < c".

2. In the case of ties, i.e. if class A and class B both have an identical value, linear interpolation is used. This is because changing the value of c infinitesimally will change both sensitivity and specificity in these cases.