Web: https://www.reddit.com/r/machinelearningnews/comments/xhnaxc/a_costsensitive_adversarial_data_augmentation/

Sept. 18, 2022, 5:53 p.m. | /u/ai-lover

machinelearningnews reddit.com

Most machine learning methods assume that each misclassification mistake a model makes is of equal severity. This is frequently not the case for unbalanced classification issues. It is typically worse to exclude a case from a minority or positive class than to incorrectly categorize an example from a negative or majority class. Several real-world instances include recognizing fraud, diagnosing a medical problem, and spotting spam emails. A false negative (missing a case) is worse or more expensive in each scenario …

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