Cost-sensitive classification is a machine learning approach that takes into account the different costs associated with misclassifying instances. In many real-world scenarios, the consequences of errors can vary significantly; for example, incorrectly diagnosing a disease may have a higher cost than misclassifying a spam email. By incorporating these costs into the classification model, it aims to minimize the overall cost of misclassifications rather than just maximizing accuracy. This technique is particularly useful in fields like healthcare, finance, and fraud detection, where the implications of errors can be severe. Cost-sensitive algorithms adjust their decision-making process to prioritize minimizing high-cost errors, leading to more effective and practical outcomes in critical applications.