Databricks Machine Learning Associate Question 245
Select 3You are training a machine learning model in Databricks to predict customer churn. You decide to use grid search combined with cross-validation to tune hyperparameters for your model. Which of the following statements correctly describes how this process works?
- A
Grid search systematically explores a specified range of hyperparameter values to find the combination that minimizes the validation error.
- B
Cross-validation splits the dataset into training and testing subsets multiple times to evaluate model performance more robustly.
- C
Grid search evaluates all possible combinations of hyperparameters on the entire dataset without splitting it.
- D
Cross-validation ensures that the model is tested on unseen data during the tuning process to reduce overfitting.
- E
Grid search uses random sampling to select specific combinations of hyperparameters for evaluation.
Show answer and explanation
Correct answers: A, B, D
Explanation
Grid search combined with cross-validation is a common approach for hyperparameter tuning in Databricks. Grid search systematically evaluates all combinations of specified hyperparameters, while cross-validation splits the dataset into folds, ensuring the model is validated on unseen data to prevent overfitting. Together, they provide a robust way to identify the optimal hyperparameters for a machine learning model.
- A. Correct.
Correct: Grid search systematically evaluates all specified hyperparameter combinations to identify the optimal ones based on model performance, such as minimizing validation error.
- B. Correct.
Correct: Cross-validation splits the dataset into folds and iteratively trains and validates the model on different subsets to provide a robust estimate of performance.
- C. Incorrect.
Incorrect: Grid search does not evaluate hyperparameters on the entire dataset; it uses validation data (often through cross-validation) to assess performance.
- D. Correct.
Correct: Cross-validation ensures the model is evaluated on unseen data by splitting the dataset, helping to prevent overfitting during hyperparameter tuning.
- E. Incorrect.
Incorrect: Random sampling of hyperparameters is characteristic of random search, not grid search.