Databricks Machine Learning Associate Question 237
Select 3You are tasked with building a machine learning model on a dataset using Databricks. To evaluate your model's performance, you want to use k-fold cross-validation. Which of the following steps correctly describe how to implement cross-validation in Databricks?
- A
Split the dataset into k equally-sized folds, train the model on k-1 folds, and validate it on the remaining fold, repeating this process k times.
- B
Use Databricks AutoML, which automatically handles cross-validation during the model training process.
- C
Manually shuffle and split the dataset into training and validation sets, evaluate the model, and repeat the process multiple times without using folds.
- D
Use Spark ML’s CrossValidator class to automate the k-fold cross-validation process.
- E
Train the model on all available data without splitting into folds, as cross-validation is not necessary in Databricks.
Show answer and explanation
Correct answers: A, B, D
Explanation
Cross-validation is a critical step in the machine learning workflow to evaluate model performance and generalization. In Databricks, k-fold cross-validation can be implemented manually by splitting the dataset into folds, or automated using tools like Databricks AutoML or Spark ML’s CrossValidator class. Manually shuffling and splitting the dataset is not equivalent to k-fold cross-validation, and skipping cross-validation altogether can lead to unreliable model evaluation.
- A. Correct.
Correct. This is the standard methodology for implementing k-fold cross-validation, which ensures that each fold is used for validation exactly once.
- B. Correct.
Correct. Databricks AutoML automates many aspects of the machine learning workflow, including cross-validation during model training and hyperparameter tuning.
- C. Incorrect.
Incorrect. Manually shuffling and splitting the dataset multiple times is not the same as performing k-fold cross-validation. This approach lacks the systematic nature of k-fold cross-validation.
- D. Correct.
Correct. Spark ML provides the CrossValidator class, which simplifies the implementation of k-fold cross-validation in a distributed environment like Databricks.
- E. Incorrect.
Incorrect. Cross-validation is necessary to evaluate the generalization of the model and avoid overfitting. Skipping cross-validation is not recommended.