Cross-validation is a special case of the validation set approach.

Question

BlackTom AI · Accepted Answer

The statement to evaluate is: 'Cross-validation is a special case of the validation set approach.'

Because no answer options are provided, we examine the claim on its own terms and contrast the two concepts.

First, consider what the validation set approach typically entails: you randomly partition the data once into a training set and a separate validation (or test) set. You train on the training portion and evaluate on the fixed validation portion. This yields a single estimate of model performance, and its reliability depends on that one split.

Next, consider cross-validation: in k-fold cross-validation, the data are split into k equally sized folds. The model is trained k times, each time using k-1 folds for training and the remaining fold for validation, and the performance estimates are averaged across the k trials. There are also variations like leave-one-out cross-validation, which is a specific case where k equals the number of observations.

From these descriptions, cross-validation is not a special case of the validation-set approach. Rather, it is a distinct methodology that generalizes the idea of validation by using multiple splits and aggregating results to obtain a more stable performance estimate. The validation-set approach provides a single split and a single evaluation, whereas cross-validation provides multiple splits and an averaged evaluation.

A common misconception might be to think cross-validation simply refers to a particular fixed validation split, which would then make it seem like a subset or variant of a single-split approach. However, the defining feature of cross-validation is the repetitive resampling and averaging across folds, which goes beyond a single validation split.

Another potential confusion is to equate cross-validation with leave-one-out cross-validation or with other resampling strategies. While these are both forms of cross-validation, they are still fundamentally different from the standard one-shot validation-set method because they involve multiple training/validation cycles instead of one.

In summary, the key distinction is that the validation set approach uses one split for evaluation, while cross-validation uses multiple splits and aggregates results. Therefore, the statement that cross-validation is a special case of the validation set approach is inaccurate.

QBUS6810 (ND) Week 6 Quiz

Cross-validation is a special case of the validation set approach.

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