Which statement about scoring data is true regarding its variables compared to training data?

The statement that scoring data can have different target variables is accurate because the purpose of scoring data is to apply a trained model to new observations that were not part of the training dataset. When you score new data, you are typically interested in predicting outcomes based on the model derived from the training data, which may not necessarily require the inclusion of the original target variable used during training.

In practice, the scoring data is expected to have the same predictor variables as in the training data to enable the model to make accurate predictions. However, it does not need to contain the same target variable since the scoring data is used primarily to generate predictions based on these predictors. Thus, the scoring process focuses on leveraging the relationships identified during training, regardless of whether the target variable is the same.

The other statements suggest restrictions on the scoring data that do not align with the practical application of model scoring. For instance, stating that score data must match all variables is too stringent because some variables may not be needed for predictions. Claiming that score data contains none of the same variables contradicts the fundamental need for predictor consistency. Lastly, the idea that score data has the same target variable only simplifies the relationship unnecessarily and overlooks the broader predictive applications of scoring data.