nn_aum_loss: AUM loss

In mlverse/tabnet: Fit 'TabNet' Models for Classification and Regression

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.

Examples

loss <- nn_aum_loss()
input <- torch::torch_randn(4, 6, requires_grad = TRUE)
target <- input > 1.5
output <- loss(input, target)
output$backward()

mlverse/tabnet documentation built on July 17, 2025, 4:15 a.m.