Loss Function
A mathematical function that quantifies the difference between predicted outputs and true values, guiding model training and optimization.
The objective metric (e.g., cross-entropy, mean squared error, custom fairness-penalty losses) that the optimizer minimizes. Choice of loss function directly influences model behavior - governance must review loss definitions to ensure alignment with business goals, ethical constraints (e.g., adding fairness regularizers), and risk tolerance before training, and must document configurations for auditability and reproducibility.
A fraud-detection model’s team chooses a weighted cross-entropy loss that penalizes false negatives four times more than false positives - reflecting the business cost of missed fraud. They document the weighting rationale and track downstream impact on both precision and recall to ensure balanced outcomes.
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