Databricks Machine Learning Associate Question 324
Select 2You are training a machine learning model using hyperparameter tuning in Databricks. You notice that as you increase the number of trials in a hyperparameter search (e.g., using HyperOpt), the model's accuracy improves. What could explain this relationship?
- A
Increasing the number of trials allows the search algorithm to explore a larger space of hyperparameter values, which increases the likelihood of finding optimal settings.
- B
More trials reduce the risk of overfitting the model to the training data by averaging the results across multiple parameter combinations.
- C
A higher number of trials helps the search algorithm avoid local optima by exploring more diverse areas of the hyperparameter space.
- D
Increasing the number of trials ensures the model's performance is evaluated more accurately on unseen data by using larger validation datasets.
Show answer and explanation
Correct answers: A, C
Explanation
Increasing the number of trials in hyperparameter optimization allows the algorithm to explore a broader and more diverse range of hyperparameter combinations. This increases the likelihood of finding optimal settings and helps prevent the algorithm from getting stuck in suboptimal regions of the hyperparameter space. However, it does not directly address overfitting or influence the size of validation datasets.
- A. Correct.
Correct. With more trials, the hyperparameter optimization process explores more combinations, increasing the chance of identifying the best-performing parameter set.
- B. Incorrect.
Incorrect. While hyperparameter tuning helps improve generalization, the number of trials itself does not directly reduce overfitting. Overfitting is typically addressed by techniques like cross-validation or regularization.
- C. Correct.
Correct. A larger number of trials allows the search algorithm to explore more of the hyperparameter space, reducing the chances of getting stuck in local optima.
- D. Incorrect.
Incorrect. The number of trials in hyperparameter tuning does not directly affect the size of validation datasets, which are typically fixed and defined by the user.