Technical GlossaryMachine Learning

Cost-Sensitive Classification

An approach that incorporates different misclassification costs into the decision process instead of treating all errors equally.

Cost-sensitive classification becomes critical in problems where the business impact of different errors is not symmetric. Missing fraud is not equivalent to raising a false alarm, just as missing a critical patient is not equivalent to triggering an unnecessary review. For that reason, the model should be evaluated not only by accuracy, but also by the economic or operational consequences of each error type. This approach aligns machine learning with real-world decision costs.