Valid Databricks-Machine-Learning-Associate Dumps shared by ExamDiscuss.com for Helping Passing Databricks-Machine-Learning-Associate Exam! ExamDiscuss.com now offer the newest Databricks-Machine-Learning-Associate exam dumps, the ExamDiscuss.com Databricks-Machine-Learning-Associate exam questions have been updated and answers have been corrected get the newest ExamDiscuss.com Databricks-Machine-Learning-Associate dumps with Test Engine here:
A data scientist uses 3-fold cross-validation and the following hyperparameter grid when optimizing model hyperparameters via grid search for a classification problem: * Hyperparameter 1: [2, 5, 10] * Hyperparameter 2: [50, 100] Which of the following represents the number of machine learning models that can be trained in parallel during this process?
Correct Answer: D
To determine the number of machine learning models that can be trained in parallel, we need to calculate the total number of combinations of hyperparameters. The given hyperparameter grid includes: Hyperparameter 1: [2, 5, 10] (3 values) Hyperparameter 2: [50, 100] (2 values) The total number of combinations is the product of the number of values for each hyperparameter: 3 (values of Hyperparameter 1)×2 (values of Hyperparameter 2)=63 (values of Hyperparameter 1)×2 (values of Hyperparameter 2)=6 With 3-fold cross-validation, each combination of hyperparameters will be evaluated 3 times. Thus, the total number of models trained will be: 6 (combinations)×3 (folds)=186 (combinations)×3 (folds)=18 However, the number of models that can be trained in parallel is equal to the number of hyperparameter combinations, not the total number of models considering cross-validation. Therefore, 6 models can be trained in parallel. Reference: Databricks documentation on hyperparameter tuning: Hyperparameter Tuning
