False negative (FN): Difference between revisions

==Introduction==

In [[binary classification]], a '''false negative''' can be defined as when the model incorrectly classifies an [[input]] into the negative [[class]] when it should have been classified as positive. For instance, in medical diagnosis tasks, false negatives may occur when models predict that patients do not have diseases when they actually do have them. Such false negatives have serious repercussions as patients may not receive appropriate treatments due to misclassified data.