Databricks Certified Machine Learning - Associate
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In a classification problem, a data scientist is optimizing model hyperparameters using 3-fold cross-validation and a specific hyperparameter grid via grid search. The hyperparameter grid includes: Hyperparameter 1 with options [4, 6, 7] and Hyperparameter 2 with options [5, 10]. What is the total number of machine learning models that can be trained simultaneously during this process? Choose only ONE best answer.
In a classification problem, a data scientist is optimizing model hyperparameters using 3-fold cross-validation and a specific hyperparameter grid via grid search. The hyperparameter grid includes: Hyperparameter 1 with options [4, 6, 7] and Hyperparameter 2 with options [5, 10]. What is the total number of machine learning models that can be trained simultaneously during this process? Choose only ONE best answer.
Explanation:
When performing a grid search with 3-fold cross-validation, the total number of models that can be trained in parallel is calculated by multiplying the number of distinct hyperparameter combinations by the number of folds. Here, Hyperparameter 1 has 3 options and Hyperparameter 2 has 2 options, resulting in 6 unique combinations. With 3 folds for each combination, the total number of models trained simultaneously is 18.