Accuracy
The fraction of predictions a model gets correct.
Accuracy measures the proportion of all predictions — both positive and negative — that a model gets right, calculated as correct predictions divided by total predictions. Though intuitive, it only tells the full story when the classes are roughly balanced. The critical exam distinction is between accuracy and more robust metrics like F1-score or AUC-ROC. SageMaker reports accuracy alongside precision, recall, and F1 in its model-quality metrics. For fraud detection or rare-disease diagnosis, where one class dominates, high accuracy can mask a model that simply ignores the minority class — making F1 or AUC the safer choice.
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