MLA-C01 Question 186
Single answerYou are working on a machine learning project where you need to optimize the hyperparameters of a model to achieve the best performance. Your dataset is large, and training the model is computationally expensive. Which hyperparameter tuning technique should you choose to efficiently explore the hyperparameter space and balance exploration with computational cost?
- A
Grid search
- B
Random search
- C
Bayesian optimization
- D
Exhaustive search
Show answer and explanation
Correct answer: C
Explanation
Bayesian optimization is the most efficient hyperparameter tuning technique in this scenario because it intelligently balances exploration and exploitation using probabilistic models. This approach reduces the number of evaluations needed while targeting the most promising hyperparameter combinations, making it ideal for computationally expensive models and large datasets.
- A. Incorrect.
Grid search systematically evaluates all possible combinations of hyperparameters. While it is effective in exploring the entire search space, it is computationally expensive and not efficient for large datasets or costly models.
- B. Incorrect.
Random search selects hyperparameter combinations randomly. It can be faster than grid search but does not intelligently adapt to prior results, leading to inefficiencies in scenarios with high computational costs.
- C. Correct.
Bayesian optimization uses probabilistic models to determine the most promising hyperparameters to evaluate next, optimizing exploration and computational efficiency. It is well-suited for scenarios where training is expensive.
- D. Incorrect.
Exhaustive search evaluates every possible combination of hyperparameters, similar to grid search, but is even more computationally prohibitive for large datasets or costly models.