Cost-sensitive learning is a machine learning approach that takes into account the different costs associated with misclassifying data points. In many real-world applications, the consequences of errors can vary significantly; for example, misclassifying a fraudulent transaction as legitimate may have a higher cost than the reverse. By incorporating these costs into the learning process, models can be trained to minimize the overall cost rather than just the number of errors. This technique is particularly useful in fields like medical diagnosis or credit scoring, where the implications of false positives and false negatives can be critical. Cost-sensitive learning adjusts the model's decision boundaries to favor outcomes that reduce the total cost, leading to more effective and responsible decision-making in sensitive applications.