How does Automated ML determine the best model?

Automated Machine Learning (AutoML) determines the best model by running multiple training iterations, which are then evaluated and scored based on specified metrics. This iterative process involves experimenting with various algorithms and hyperparameters to identify the configuration that minimizes errors or maximizes predictive accuracy based on the dataset provided.

During this process, AutoML automatically selects appropriate models and parameters while analyzing their performance. By utilizing a scoring method—such as accuracy, precision, recall, or mean squared error—AutoML can objectively compare various model candidates. This ensures that the selected model is the most effective for the given data and task.

The other choices do not capture the comprehensive nature of how AutoML functions. A single training session would not provide enough variance to find the most suitable model; this would likely lead to suboptimal results. Manual intervention by data scientists is more time-consuming and less efficient than the automated process designed to streamline model selection. Lastly, relying solely on historical data without the iterative testing process would not allow for the exploration of different models and tuning of hyperparameters necessary to achieve optimal performance.