How does Amazon SageMaker compare models based on their accuracy?

Amazon SageMaker employs automatic model tuning and evaluation metrics tracking to compare models based on their accuracy. This approach leverages several built-in features within SageMaker that facilitate both the optimization of hyperparameters and the evaluation of model performance.

Automatic model tuning, also known as hyperparameter optimization, enables SageMaker to automatically search for the best-performing parameters for a given model. This process involves running multiple training jobs with different parameter combinations and assessing their performance based on specified evaluation metrics, such as accuracy. By automatically adjusting these hyperparameters, SageMaker can identify which combination yields the highest accuracy without requiring manual intervention.

Additionally, SageMaker tracks evaluation metrics during training and can assess models against various performance criteria, allowing users to make data-driven decisions when selecting the best model. This comprehensive evaluation process ensures that users can efficiently compare models and select the one that will perform best in real-world applications.

The other choices do not align with the functionality of Amazon SageMaker. Manual comparisons would lack the efficiency and thoroughness provided by automated tools. Altering datasets does not inherently contribute to the direct comparison of models' accuracies. A/B testing, though an effective evaluation method in some scenarios, is just one strategy and does not encompass the broader automatic tuning and tracking capabilities that SageMaker