Cost-sensitive learning
Cost-sensitive learning is a machine learning approach that takes into account costs associated with misclassifications of different classes during the model training process. In traditional machine learning, the focus is on maximizing overall accuracy, but in many real-world scenarios, misclassifying certain classes can have more severe consequences than misclassifying others.
For example, in a medical diagnosis application, misdiagnosing a severe disease as not present (false negative) could have more significant consequences than misdiagnosing a mild condition as present (false positive). In fraud detection, incorrectly flagging a legitimate transaction as fraudulent (false positive) might inconvenience the customer, while failing to detect actual fraudulent transactions (false negative) could lead to significant financial losses.
Cost-sensitive learning addresses these imbalances in costs by assigning different misclassification costs to different...
