Not All Mistakes Are Created Equal: Cost-sensitive Learning

In classification problems, we often assume that every misclassification is equally bad. Consider the example of trying to classify whether or not there is a terrorist threat. There are two types of misclassifications: either we predict there is a threat but there is actually no threat (false positive), or we predict there is no threat but there actually is a threat (false negative). Clearly the false negative is much more dangerous than the false positive -- we might end up wasting time and money in the false positive case, but people might die in the false negative case. We call classification problems like this cost-sensitive.