Cost-sensitive Learning

Cost-sensitive learning is a machine learning approach that takes into account the cost of misclassification errors. In traditional machine learning, the goal is to minimize the overall error rate, regardless of the cost of different types of errors. However, in many real-world applications, the cost of false positives and false negatives can be very different. For example, in medical diagnosis, a false negative (failing to diagnose a disease) can be much more costly than a false positive (diagnosing a disease that is not present). Cost-sensitive learning algorithms adjust the classification threshold to minimize the expected cost of misclassification. This approach can improve the overall accuracy of the model and reduce the cost of errors in practical applications.
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