Databricks Machine Learning Associate Question 587
Select 3You are training a machine learning model using Databricks' MLlib, and you want to evaluate its performance using k-fold cross-validation to avoid overfitting. Which of the following steps are necessary to correctly implement cross-validation in this scenario?
- A
Define a parameter grid for hyperparameter tuning, if applicable.
- B
Split the dataset into training and test sets before applying k-fold cross-validation.
- C
Use a cross-validator object to repeatedly train and validate the model on k subsets of the data.
- D
Manually iterate through each fold in the cross-validation process and compute metrics for each fold.
- E
Specify the evaluation metric to assess model performance.
Show answer and explanation
Correct answers: A, C, E
Explanation
To correctly implement cross-validation in Databricks, you need to use tools like CrossValidator, which automate the process of splitting the data into folds and evaluating performance. Specifying an evaluation metric is critical to measure the model's success during each fold. Defining a parameter grid is essential if hyperparameter tuning accompanies cross-validation. Splitting the data manually or manually iterating through folds is unnecessary because the CrossValidator handles these tasks.
- A. Correct.
Defining a parameter grid is necessary if you are performing hyperparameter tuning alongside cross-validation. While not mandatory for basic cross-validation, it is an essential step if you want to optimize model parameters.
- B. Incorrect.
Splitting the dataset into training and test sets is not required before k-fold cross-validation, as the process inherently splits the data into training and validation sets for each fold.
- C. Correct.
Using a cross-validator object, such as CrossValidator in Databricks MLlib, automates the k-fold cross-validation process, making it an essential step.
- D. Incorrect.
Manually iterating through each fold is not necessary when using tools like CrossValidator in Databricks, as they handle the process automatically.
- E. Correct.
Specifying an evaluation metric is crucial for assessing the model's performance during cross-validation, as it determines how well the model is performing on the validation sets.