You are building a prediction model to develop a tool that can diagnose a particular disease so that individuals with the disease can receive…

A false negative is an error where a positive case (belonging to the target class) is incorrectly

predicted as negative (not belonging to the target class). A false negative rate is the ratio of false

negatives to all actual positive cases. A low false negative rate means that most of the positive cases

are correctly identified by the classifier.

For a diagnostic tool that can diagnose a particular disease so that individuals with the disease can

receive treatment, it is of primary importance that it has a low false negative rate. This is because

false negatives can have serious consequences for patients who have the disease but do not receive

treatment, such as increased risk of mortality or complications. A low false negative rate can ensure

that most patients who have the disease are diagnosed correctly and receive timely treatment.