You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness .
Which actions should you take? Choose 2 answers
A . Decrease the number of parallel trials
B . Decrease the range of floating-point values
C . Set the early stopping parameter to TRUE
D . Change the search algorithm from Bayesian search to random search.
E . Decrease the maximum number of trials during subsequent training phases.
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Can you please post explanations in at least one line for all answers?