What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

Explanations:

Root Mean Square Error (RMSE) measures the magnitude of prediction errors but does not indicate the direction (overestimation or underestimation) of these errors.

Residual plots graphically show the differences between predicted and actual values, allowing the Specialist to assess whether the model tends to overestimate or underestimate the target by observing the distribution of residuals.

Area under the curve (AUC) is a metric used primarily for classification models to evaluate the trade-off between true positive and false positive rates. It does not provide information about overestimation or underestimation in regression models.

A confusion matrix is used to evaluate the performance of classification models, indicating the counts of true positives, false positives, true negatives, and false negatives, but it does not apply to regression analysis or provide insight into prediction errors.