Databricks Machine Learning Associate Question 589
Select 3You are tasked with evaluating the performance of a machine learning model using cross-validation in Databricks. Which of the following steps are necessary to correctly implement cross-validation as part of the model fitting process?
- A
Split the dataset into training and validation sets using random sampling.
- B
Specify a cross-validator object with the desired number of folds.
- C
Use the cross-validation method to both train the model and evaluate its performance.
- D
Manually split the dataset into k subsets and train the model on each split individually.
- E
Ensure hyperparameters are tuned within the cross-validation process if required.
Show answer and explanation
Correct answers: B, C, E
Explanation
Cross-validation is a robust technique to evaluate model performance while reducing overfitting and ensuring generalizability. In Databricks, it requires specifying a cross-validator object (e.g., using MLlib or scikit-learn), training and evaluating the model through the cross-validation process, and optionally tuning hyperparameters to improve performance. Random splitting or manual partitioning is not specific to the cross-validation process, as these steps are typically automated within cross-validation frameworks.
- A. Incorrect.
While splitting the dataset into training and validation sets is a common practice, this is not specific to cross-validation. Cross-validation involves systematically rotating through subsets of the data, rather than a single random split.
- B. Correct.
Specifying a cross-validator object with the desired number of folds is essential for implementing cross-validation, as it controls how the data is partitioned into training and validation sets.
- C. Correct.
Cross-validation inherently involves training the model on different training sets and evaluating it on corresponding validation sets, making this step a crucial part of its process.
- D. Incorrect.
Manually splitting the dataset into k subsets is not necessary, as cross-validation methods (e.g., KFold in scikit-learn) automate this process.
- E. Correct.
Hyperparameter tuning is often integrated into the cross-validation process to optimize the model's performance, making this step important when required.