tl;dr with the
glmnet package, is it possible to optimize for the area under the precision-recall curve, rather than the area under the ROC curve?
I am using the
glmnet package in
R to perform elastic-net penalized logistic regression for binary classification on a severely class unbalanced dataset, using
type.measure = 'auc' to optimize the area under the curve (AUC) of the receiver operator characteristic (ROC), during cross-validation to select an elastic-net lambda parameter.
However, with severely imbalanced datasets, it appears that area under the Precision-Recall (PRC) curve may be preferable to ROC AUC; e.g., Saito 2015.
This does not seem to be a
type.measure option in
cv.glmnet. Has anyone found a way to use
glmnet logistic regression with PRC-AUC? If not, how important do people think it is to use PRC and not ROC for a severely class imbalanced target?