Which parameter sweep mode should you use?

You create a binary classification model by using Azure Machine Learning Studio.

You must tune hyperparameters by performing a parameter sweep of the model.

The parameter sweep must meet the following requirements:

✑ iterate all possible combinations of hyperparameters

✑ minimize computing resources required to perform the sweep

✑ You need to perform a parameter sweep of the model.

Which parameter sweep mode should you use?
A . Random sweep
B. Sweep clustering
C. Entire grid
D. Random grid
E. Random seed

View Answer

Answer: D

Explanation:

Maximum number of runs on random grid: This option also controls the number of iterations over a random sampling of parameter values, but the values are not generated randomly from the specified range; instead, a matrix is created of all possible combinations of parameter values and a random sampling is taken over the matrix. This method is more efficient and less prone to regional oversampling or undersampling.

If you are training a model that supports an integrated parameter sweep, you can also set a range of seed values to use and iterate over the random seeds as well. This is optional, but can be useful for avoiding bias introduced by seed selection.