You have an Azure Machine Learning workspace. You plan to implement automated hyperparameter tuning for model training in the workspace. You need to select the sweep jobs parameter sampling method tha

The correct answer is A. Sobol. Sobol sampling is a quasi-random, low-discrepancy sequence method. Unlike true random sampling, Sobol uses a deterministic mathematical sequence that covers the search space evenly while remaining reproducible - running the sweep again with the same configuration produces the sam

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Question

You have an Azure Machine Learning workspace. You plan to implement automated hyperparameter tuning for model training in the workspace. You need to select the sweep jobs parameter sampling method that will randomize the selection of hyperparameters from the search space but allow for reproducing search results. Which sampling method should you use?

Options

ASobol
BGrid
CRandom
DBayesian

How the community answered

(20 responses)

A
80% (16)
B
5% (1)
C
10% (2)
D
5% (1)

Explanation

Sobol sampling is a quasi-random, low-discrepancy sequence method. Unlike true random sampling, Sobol uses a deterministic mathematical sequence that covers the search space evenly while remaining reproducible - running the sweep again with the same configuration produces the same sequence of hyperparameter combinations. Grid search is exhaustive (not randomized). Random sampling is non-deterministic and cannot reproduce exact results without a fixed seed. Bayesian sampling uses prior results to guide the next selection, making it adaptive rather than reproducible in the same sense.

Topics

#Hyperparameter Tuning#Azure Machine Learning#Sweep Jobs#Sobol Sampling

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