Cost-sensitive learning is a subfield of machine learning focused on developing algorithms that explicitly account for and minimize the cumulative cost of errors, rather than solely optimizing for metrics like accuracy which treat all errors equally. It investigates methods to incorporate differential penalties associated with various misclassification types or decision outcomes, making it a critical approach in applications where the economic, social, or safety consequences of different errors are asymmetric.