In cross-validation, data are split randomly into two halves, and a regression model is estimated for each half and compared, which assessment method is described?

In cross-validation, data are split randomly into two halves, and a regression model is estimated for each half and compared, which assessment method is described?

• A. It uses bootstrapping to approximate sampling distribution
• B. It splits data into training and testing sets but not necessarily two halves
• C. It uses leave-one-out approach exclusively
• D. It is assessed by data splitting into two halves and comparing models

Answer: (D)

The idea being tested is assessing model performance by using a simple cross-validation approach that splits the data into two equal parts. In this two-fold setup, you fit the regression model on one half and evaluate or compare its predictions on the other half, then swap the roles of the halves. This provides an estimate of how well the model generalizes to new data because the evaluation happens on data not used to train the model. Bootstrapping, by contrast, builds many training sets by sampling with replacement and is used to approximate the sampling distribution, not just to split data into two halves for direct model comparison. Leave-one-out cross-validation involves leaving out one observation at a time and is more granular than a simple two-half split. A general training/testing split describes using separate training and testing sets but doesn’t specify two halves, so it’s not as precise for the described method.

The idea being tested is assessing model performance by using a simple cross-validation approach that splits the data into two equal parts. In this two-fold setup, you fit the regression model on one half and evaluate or compare its predictions on the other half, then swap the roles of the halves. This provides an estimate of how well the model generalizes to new data because the evaluation happens on data not used to train the model.

Bootstrapping, by contrast, builds many training sets by sampling with replacement and is used to approximate the sampling distribution, not just to split data into two halves for direct model comparison. Leave-one-out cross-validation involves leaving out one observation at a time and is more granular than a simple two-half split. A general training/testing split describes using separate training and testing sets but doesn’t specify two halves, so it’s not as precise for the described method.