Cross-Entropy Loss

Cost-Sensitive Learning is a machine learning approach that emphasizes minimizing the overall cost of errors rather than just error rate. It assigns different weights to various misclassification types, such as false positives and false negatives, based on their respective consequences. This technique helps develop models better aligned with real-world priorities, especially in applications where certain errors are more costly than others.