Databricks Machine Learning Associate Question 585
Select 3You are tasked with evaluating the performance of a machine learning model using a limited dataset. You are considering whether to use cross-validation or a simple train-validation split for model evaluation. Which of the following are benefits of using cross-validation over a train-validation split?
- A
Cross-validation provides a more reliable estimate of model performance by averaging results across multiple folds.
- B
Cross-validation requires less computation time compared to a train-validation split.
- C
Cross-validation mitigates the risk of performance estimates being skewed by a single train-validation split.
- D
Cross-validation is more suitable for hyperparameter tuning compared to a train-validation split.
Show answer and explanation
Correct answers: A, C, D
Explanation
Cross-validation is a robust method for model evaluation, especially when working with limited data. It provides a more reliable estimate of model performance by averaging results over several folds, reducing the risk of bias or variance from a single split. While it is computationally more expensive than a train-validation split, its ability to provide consistent performance measures and support hyperparameter tuning makes it a preferred choice in many scenarios.
- A. Correct.
Correct: Cross-validation averages model performance over multiple folds, reducing the impact of variance caused by the specific split of the data.
- B. Incorrect.
Incorrect: Cross-validation typically requires more computation time because the model is trained and evaluated multiple times, once for each fold.
- C. Correct.
Correct: By splitting the data into multiple folds, cross-validation reduces the risk of relying on a single, potentially unrepresentative train-validation split.
- D. Correct.
Correct: Cross-validation provides a robust way to evaluate different hyperparameter settings by ensuring performance is assessed across multiple subsets of the data.