Databricks Machine Learning Associate Question 236
Select 3You are working on a machine learning project in Databricks and want to evaluate the performance of several models while tuning hyperparameters. To ensure robust evaluation, you decide to perform cross-validation. Which of the following steps correctly describe how to implement cross-validation in this context?
- A
Split the dataset into k folds and use each fold as a validation set while training on the remaining k-1 folds.
- B
Use the Databricks
CrossValidatorclass to automatically perform k-fold cross-validation with a specified set of hyperparameters. - C
Evaluate the model on the training data only, as this is sufficient to assess model performance during cross-validation.
- D
Specify a parameter grid for hyperparameter tuning and pass it to the cross-validation process for automated model selection.
- E
Manually split the data into k folds and write custom code to train and validate the model on each fold.
Show answer and explanation
Correct answers: A, B, D
Explanation
Cross-validation is a key technique for evaluating model performance and selecting the best hyperparameters. In Databricks, the CrossValidator class simplifies this process by automating k-fold cross-validation and integrating hyperparameter tuning. Splitting the dataset into k folds and specifying a parameter grid are essential steps in this process, while manually implementing cross-validation or relying solely on training data for evaluation is not recommended.
- A. Correct.
Correct: This is the fundamental concept of k-fold cross-validation, where the dataset is split into k parts to ensure the model is evaluated on different subsets of the data.
- B. Correct.
Correct: Databricks provides a
CrossValidatorclass that simplifies the process of performing k-fold cross-validation, making it easier to integrate into a machine learning workflow. - C. Incorrect.
Incorrect: Evaluating the model on training data only does not provide an unbiased estimate of model performance, which is the goal of cross-validation.
- D. Correct.
Correct: Specifying a parameter grid allows for systematic hyperparameter tuning during cross-validation, enabling the selection of the best-performing parameters.
- E. Incorrect.
Incorrect: While manually implementing k-fold cross-validation is possible, using automated tools like Databricks
CrossValidatoris recommended for efficiency and reliability.