In a 75%/25% Cross-Validation

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BlackTom AI · Accepted Answer

Starting with the question: 'In a 75%/25% Cross-Validation', we need to evaluate what this cross-validation scheme entails and examine each offered option.
Option 1: 'The model is developed on 25% of the data, and tested on the other 75%.' This describes a 25% training and 75% testing split, which is the inverse of a 75%/25% cross-validation. In standard 75%/25% cross-validation, we would expect the training set to be 75% and the test set to be 25%. Therefore this option misstates the typical arrangement and is incorrect.
Option 2: 'If the AIC is less than 2%, you have negative shrinkage.' AIC (Akaike Information Criterion) values are used to compare models, with lower AIC indicating a better trade-off between fit and complexity. The statement about 'AIC less than 2%' and 'negative shrinkage' introduces terminology that is not standard for AIC interpretation and mischaracterizes shrinkage concepts; shrinkage is a term associated with regularization or bias-variance trade-offs, not something tied to a fixed 'less than 2% AIC' threshold. This makes the claim incorrect or at least misleading.
Option 3: 'if the shrinkage in R2 is greater than 2%, the cross-validation is successful' This option ties cross-validation success to a specific threshold in R-squared shrinkage, which is not a standard or universally accepted criterion for assessing cross-validation success. Shrinkage of R2 is not a widely used or agreed-upon rule for evaluating cross-validation performance; cross-validation success is usually judged by predictive error or stability across folds, not a fixed 'greater than 2%' shrinkage in R2. Therefore, this statement is not a reliable or correct characterization.
Option 4: 'The model is developed on 75% of the data, and tested on the other 25%' This aligns with the conventional interpretation of a 75%/25% cross-validation setup: training occurs on 75% of the data, and evaluation is conducted on the remaining 25%. It reflects the standard practice for this split and is consistent with how cross-validation partitions are commonly described. This makes the statement correct in the context of a 75%/25% cross-validation scheme.
In summary, the first option reverses the split, the second and third introduce inaccurate or misleading criteria related to AIC and R2 shrinkage, while the fourth correctly describes the intended data partitioning for a 75%/25% cross-validation.

In a 75%/25% Cross-Validation

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