If the outcome proportions in training and scoring populations do not match, what is the effect on model prediction estimates?

When the outcome proportions in training and scoring populations do not match, this can lead to biased predictions in a model. In this scenario, the model may have been trained on data that does not reflect the distribution of the actual population it will be scoring. This misalignment can cause it to produce predictions that are skewed or not representative of the real-world conditions, leading to compromised model performance.

The choice indicating that the predictions will not be biased emphasizes that, in a perfect world, predictions would align with the true outcome regardless of training data distribution. However, when there is a mismatch in outcome proportions, the expectation is that biases will emerge due to the differences in data characteristics and distributions. Thus, while the phrasing might suggest a more optimistic view, it is crucial to understand that in practice, significant discrepancies in populations typically result in biases in predictions.

Other options suggest outcomes such as high accuracy or improvement, which are overly optimistic given the context of mismatched outcomes. Models trained on distributions that differ significantly from the execution or scoring environment often struggle to generalize, leading to subpar predictive performance rather than any improvements or high accuracy.