Azure Data Scientists Associate Practice Exam

Grid sampling is the appropriate method for trying every possible combination of discrete hyperparameter values. This technique systematically explores the hyperparameter space by creating a grid of hyperparameter values and then evaluating the model at each point in that grid. This exhaustive approach ensures that all combinations are considered, which allows for a comprehensive search over the defined hyperparameter set.

For example, if you have hyperparameters for a model with two different categorical values for each parameter, grid sampling would methodically evaluate the model at every combination of those values, allowing you to understand how each combination impacts model performance.

In contrast, random sampling randomly selects combinations from the hyperparameter space, which may lead to some combinations being skipped entirely. Bayesian sampling uses a probabilistic model to guide the selection of hyperparameters, making it less exhaustive in exploring all possible combinations. Sequential sampling involves evaluating the model over time, typically focusing on the most promising areas of the parameter space based on previous evaluations, rather than ensuring every possible combination is tested.