The Bounded Symmetric ROC fitness function
is based on the
area under the ROC curve (AUC ROC),
giving equal weight to models symmetrically distributed around the 0.5 value of the AUC ROC.
It allows you to set boundaries for the model output,
which the learning algorithms will try to approach.
The Bounded Symmetric ROC fitness function can be combined
with a cost matrix in order to impose specific constraints on the
In addition, the evolvable
logistic threshold, which is intrinsic to the
logistic regression model, can be adjusted by changing the number of bins.