Which is not a benefit of using cross-validation for model evaluation?

Question

BlackTom AI · Accepted Answer

Here are the options examined in turn, with reasoning for why each is or isn’t a benefit of cross-validation.
Option 1: 'It provides statistics on estimated performance so that we can understand how performance will vary across data sets.' This is indeed a benefit of cross-validation. By dividing data into folds and evaluating across folds, we obtain an estimate of how performance may vary across different samples, which informs us about the stability and expected variability of the model.
Option 2: 'It can illustrate whether obtaining more data would be a good investment.' This statement is tricky. Cross-validation can help assess model performance on the available data, but it does not directly answer the question of whether collecting more data would improve performance. In practice, learning curves or experiments with additional data are usually needed to determine the value of more data. Therefore, this is not a core, direct benefit of cross-validation itself.
Option 3: 'It makes better use of limited data by using all data for both training and testing.' This is a nuanced claim. Cross-validation does ensure that every data point is used for testing in some fold and used for training in other folds, so across folds it makes efficient use of limited data. However, saying it uses 'all data for both training and testing' in a single model run is misleading, since in any given fold a point is either in training or testing, not both. The statement is partially accurate in spirit but can misrepresent how CV works, which can mislead about its fundamental mechanism. As a benefit, it’s acceptable but its wording is potentially misleading.
Option 4: 'It provides an estimate of generalization performance.' This is a classic and well-established benefit of cross-validation. It gives an empirical assessment of how the model is expected to perform on unseen data, which is central to model evaluation.
In summary, the statement most typically considered not a direct benefit (or is at least ambiguously phrased) is Option 3, because it overstates the way cross-validation uses data and can mislead about the training/testing split within a single run. The other options describe standard, direct benefits of cross-validation, with option 2 being the least direct benefit since it addresses data collection decisions rather than evaluation per se.

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Which is not a benefit of using cross-validation for model evaluation?

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