Databricks Machine Learning Associate Question 232
Select 3A data scientist is working on a machine learning project and wants to evaluate the performance of a regression model. They are deciding whether to use cross-validation or a simple train-validation split. Which of the following are potential benefits of using cross-validation over a train-validation split?
- A
Cross-validation provides a more reliable estimate of the model's generalization performance.
- B
Cross-validation reduces the risk of overfitting by using a larger portion of the data for training.
- C
Cross-validation is computationally cheaper than a train-validation split since it uses fewer data partitions.
- D
Cross-validation helps identify if the model's performance varies significantly across different subsets of the data.
- E
Cross-validation eliminates the need for hyperparameter tuning.
Show answer and explanation
Correct answers: A, B, D
Explanation
Cross-validation is a powerful model evaluation technique that provides reliable performance estimates by averaging results across multiple folds. It mitigates biases introduced by a single train-validation split and allows for better usage of the dataset for both training and testing. However, it requires more computational resources due to repeated model training, and it does not replace the need for hyperparameter tuning.
- A. Correct.
Correct: Cross-validation averages the performance over multiple folds, providing a more robust and reliable estimate of how the model will generalize to unseen data compared to a single train-validation split.
- B. Correct.
Correct: Since cross-validation uses multiple train-test splits, it ensures that a larger portion of the data is used for training across the folds, reducing the bias in training data allocation.
- C. Incorrect.
Incorrect: Cross-validation is computationally more expensive than a single train-validation split because it requires training the model multiple times on different subsets of the data.
- D. Correct.
Correct: By evaluating the model on different subsets, cross-validation can reveal if there is high variance in performance across folds, highlighting data distribution issues or model instability.
- E. Incorrect.
Incorrect: Cross-validation is a technique for model evaluation and does not eliminate the need for hyperparameter tuning, which is a separate optimization process.