A data scientist is performing hyperparameter tuning using an iterative optimization algorithm. Each evaluation of unique hyperparameter values is being trained on a single compute node. They are performing eight total evaluations across eight total compute nodes. While the accuracy of the model does vary over the eight evaluations, they notice there is no trend of improvement in the accuracy. The data scientist believes this is due to the parallelization of the tuning process. Which change could the data scientist make to improve their model accuracy over the course of their tuning process?
Select one answer.
Purchase Full Access to view answer options & explanation
The first 62 questions in this bank are completely free. Unlock Full Access on the exam page to view all answer choices, verified answers, explanations, and community discussions for all 205 questions.
