nn_aum_loss {torch} | R Documentation |
AUM loss
Description
Creates a criterion that measures the Area under the Min(FPR, FNR)
(AUM) between each
element in the input pred_tensor
and target label_tensor
.
Usage
nn_aum_loss()
Details
This is used for measuring the error of a binary reconstruction within highly unbalanced dataset,
where the goal is optimizing the ROC curve. Note that the targets label_tensor
should be factor
level of the binary outcome, i.e. with values 1L
and 2L
.
References
J. Hillman, T.D. Hocking: Optimizing ROC Curves with a Sort-Based Surrogate Loss for Binary Classification and Changepoint Detection https://jmlr.org/papers/volume24/21-0751/21-0751.pdf
Examples
if (torch_is_installed()) {
loss <- nn_aum_loss()
input <- torch_randn(4, 6, requires_grad = TRUE)
target <- input > 1.5
output <- loss(input, target)
output$backward()
}
[Package torch version 0.15.1 Index]