Cost-Sensitive Learning

Cost-sensitive learning is a machine learning approach that takes into account the costs associated with different types of classification errors. Unlike traditional methods that treat all errors equally, cost-sensitive learning assigns varying costs to different misclassifications, allowing for more nuanced decision-making. This is particularly useful in domains where the consequences of false positives and false negatives differ significantly, such as in medical diagnosis or fraud detection. By incorporating cost information into the learning process, models can be optimized to minimize the total expected cost rather than just maximizing accuracy.