False negative (FN): Difference between revisions

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 ==Introduction==
-In machine learning, a false negative (FN) occurs when a model predicts a negative outcome for an input when the true outcome is positive. In other words, this occurs when the model fails to identify positive instances correctly. False negatives are frequently linked with Type II errors in statistics - when one fails to reject a null hypothesis when it is actually false.
+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.
-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.
 ==How to measure False Negatives?==