In situations where you wish to reduce the number of false negatives, you use low precision and high recall. Let me explain what false negative with an example. Suppose you have a dataset of people affected by a disease. You wish to find out people who are affected by the disease. Anybody who is affected shouldn't be classified as not affected. This is called a false negative.