Databricks Machine Learning Associate Question 247
Select 3You are building a machine learning model in Databricks and want to optimize the hyperparameters of a Random Forest classifier. You decide to use grid-search combined with cross-validation. Which of the following statements accurately describe this process?
- A
Grid-search tests all possible combinations of hyperparameters within the specified ranges.
- B
Cross-validation ensures that the model is trained and evaluated on different subsets of the dataset to minimize overfitting.
- C
Grid-search automatically selects the best model by testing it on a separate test dataset.
- D
Cross-validation splits the dataset into training and validation sets for each fold, but does not use the test dataset during the grid-search process.
- E
Grid-search is computationally efficient because it avoids testing all hyperparameter combinations.
Show answer and explanation
Correct answers: A, B, D
Explanation
Grid-search combined with cross-validation is a methodical approach to hyperparameter tuning. Grid-search evaluates all possible combinations of hyperparameters, while cross-validation ensures that the model evaluation is reliable by training and validating across multiple folds. The test dataset is not involved during this process, as it is reserved for final model evaluation to avoid data leakage. Although grid-search is exhaustive, it can be computationally expensive due to the large number of hyperparameter combinations evaluated.
- A. Correct.
Correct: Grid-search systematically evaluates all possible combinations of specified hyperparameter values to find the best-performing set.
- B. Correct.
Correct: Cross-validation ensures robust model evaluation by splitting the data into multiple folds, thereby reducing the risk of overfitting.
- C. Incorrect.
Incorrect: Grid-search does not use the test dataset to select the best model. It relies on cross-validation scores to identify the optimal hyperparameters.
- D. Correct.
Correct: Cross-validation uses training and validation sets within each fold and does not involve the test dataset during the grid-search process. The test dataset is reserved for final evaluation.
- E. Incorrect.
Incorrect: Grid-search is not computationally efficient, as it evaluates every combination of hyperparameters, which can be resource-intensive.