What is one advantage of k-fold cross-validation over a single holdout set?

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Question: What is one advantage of k-fold cross-validation over a single holdout set?

Option 1: 'It provides a more stable estimate of model performance' This is accurate because k-fold cross-validation averages performance across multiple folds, reducing variance due to a particular train/test split and yielding a more dependable assessment of how the model will generalize.

Option 2: 'It guarantees higher accuracy' This is incorrect. Cross-validation does not guarantee that the model’s accuracy on unseen data will be higher; it provides a more reliable estimate of performance, but the actual accuracy can still be better or worse on a new dataset depending on data and model.

Option 3: 'It avoids the need to tune parameters' This is false. Cross-validation can be used in hyperparameter tuning (e.g., grid search with cross-validation), but it does not inherently eliminate the need for parameter tuning.

Option 4: 'It is faster to compute' This is generally not true. In practice, k-fold cross-validation is more computationally intensive than evaluating a model on a single holdout set because the model is trained k times on different subsets of data.

In summary, the key practical benefit of k-fold cross-validation over a single holdout is the reduction in variance of the performance estimate, leading to a more stable and reliable evaluation across folds.

COMM_V 414 103 2025W1 Concept Check 3 (individual)

What is one advantage of k-fold cross-validation over a single holdout set?

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