Miguel Ángel

…we can accept a low precision if the cost of the follow-up examination is not significant. However, in cases where we want to find an optimal blend of precision and recall we can combine the two metrics using what is called the F1 score.

The terrorist detection task is an imbalanced classification problem: we have two classes we need to identify — terrorists and not terrorists — with one category representing the overwhelming majority of the data points. Another imbalanced classification problem occurs in disease detection when the rate of the disease in the public is very low. In both these cases the positive class — disease or terrorist — is greatly outnumbered by the negative class. These types of problems are examples of the fairly common case in data science when accuracy is not a good measure for assessing model performance.

The terrorist detection task is an imbalanced classification problem: we have two classes we need to id…