A data scientist is tuning a cuML RandomForestClassifier and wants to explore a large search space across six hyperparameters efficiently. She has GPU capacity that makes training each candidate model fast. Which search strategy best exploits that advantage while covering the space more effectively than an exhaustive approach?
- ABayesian optimisation, which builds a probabilistic surrogate model to direct subsequent trials toward promising hyperparameter regions Correct
- BGrid search, which tests every combination of the predefined values
- CRandom search, which samples hyperparameter combinations from the defined distributions
- DManual search, where the data scientist adjusts one hyperparameter at a time based on intuition
Why A is correct: Bayesian optimisation uses the results of previous trials to update a surrogate model and select the next candidates intelligently, converging on good configurations with far fewer evaluations than grid or random search - a strategy that becomes practical when GPU acceleration makes each trial fast.
Why B is wrong: Grid search is exhaustive and scales exponentially with the number of hyperparameters, making it impractical for a six-parameter space even with fast GPU training.
Why C is wrong: Random search samples the space without learning from prior trials, so it is more efficient than grid search but still does not direct subsequent trials toward promising regions.
Why D is wrong: Manual one-at-a-time tuning ignores interactions between hyperparameters and relies entirely on the practitioner's intuition, making it the least systematic and least reproducible strategy available.