Penalize high confident false negative classifications?
## ❓ Questions & Help
I added the line `logits = torch.nn.functional.softmax(logits)` to convert binary classifications to a confidence score between 0.0 - 1.0. However, the predictions are very harsh being really close to either 0.0 or 1.0 and not somewhere in between. Is there a way to penalize the model from being so categorical? I especially want to minimize high confidence scores on false negatives.
The softmax function specifically uses exponentiation to exacerbate the differences in scores (to get the soft 'max'). You can normalize scores by other means than a softmax.

Related to your title: using a log loss will penalize wrong predictions with high confidence more (e.g. BCE).<|||||>Related read is the section entitled "Don’t Mistake Class Probabilities for Confidence" here: https://www.inovex.de/blog/uncertainty-quantification-deep-learning/<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
