Which of the following values represents the overall cross-validation root-mean-squared error?

A data scientist uses 3-fold cross-validation when optimizing model hyperparameters for a regression problem.

The following root-mean-squared-error values are calculated on each of the validation folds:

• 10.0

• 12.0

• 17.0

Which of the following values represents the overall cross-validation root-mean-squared error?
A . 13.0
B . 17.0
C . 12.0
D . 39.0
E . 10.0

View Answer

Answer: A

Explanation:

To calculate the overall cross-validation root-mean-squared error (RMSE), you average the RMSE values obtained from each validation fold. Given the RMSE values of 10.0, 12.0, and 17.0 for the three folds, the overall cross-validation RMSE is calculated as the average of these three values:

Overall CV RMSE=10.0+12.0+17.03=39.03=13.0Overall CV RMSE=310.0+12.0+17.0=339.0=13.0

Thus, the correct answer is 13.0, which accurately represents the average RMSE across all folds.

Reference: Cross-validation in Regression (Understanding Cross-Validation Metrics).