Typically prediction is about getting the right answer. But many prediction problems have large and asymmetric costs for different types of mistakes. And often, the chance of making mistakes is exacerbated by training data imbalances. Cost-Sensitive Learning is the range of techniques for extending standard ML approaches to deal with imbalanced data and outcomes. Cost-sensitive predictions will instead favour the most valuable or lowest risk answers.