You plan to tune model hyperparameters using a sweep job in your Azure Machine Learning workspace.

You need to select a sampling method that supports both early termination of low-performance runs and the use of continuous hyperparameters.

Proposed solution: Use the Bayesian sampling method over the hyperparameter space.

Does this solution meet the goal?