Which statement about scoring data in model implementation is true?

When scoring data in model implementation, it is essential to understand how the scoring process works. The correct statement indicates that score data includes the same input variables as the training data, which is crucial because the model is built using these input variables. This similarity ensures that the model can appropriately apply the learned patterns to make predictions on new data.

However, the target variable may be different or absent in the score data. In many instances, the purpose of scoring is to predict the target variable for new, unseen instances where the true target value isn't available yet—such as in deployment scenarios where you're scoring customer data to determine likelihoods of purchase or risk. Thus, this reflects the typical situation in predictive modeling where the target variable can be unknown in the context of scoring.

In contrast, the other statements do not hold true. For instance, scoring data does not include the same target variable as training data in every case (refuting the first statement) and it does not lack essential input variables, as these inputs are fundamental for the scoring process (refuting the second statement). Moreover, stating that all variables must be the same conflicts with typical practices where scoring datasets may contain additional observations or a focused subset tailored to a specific analysis, but the core input variables are