What could happen if you do not adjust for separate sampling?

When you do not adjust for separate sampling in your model training and evaluation, various issues can arise that fundamentally compromise the validity of your predictions and assessments.

Firstly, prediction estimates could reflect the target proportions in the training sample rather than the general population. If your training data is not representative of the overall population, the model learned will inherently skew its predictions based on this unrepresentative sample, ultimately leading to inaccurate predictions when applied to new, unseen data.

Secondly, decision-based statistics concerning misclassification can misrepresent model performance. If you have an imbalanced dataset and don't account for it, you might find that the model appears to perform well within the context of your training dataset, but in reality, it may fail to generalize effectively to the broader population—resulting in a false sense of confidence in the model's predictive ability.

Lastly, score ranking plots, which are essential for understanding how well your model differentiates between classes, may also be inaccurate. Without adjustments for separate sampling, the plot could exhibit misleading trends, compromising your ability to assess the model's performance reliably.

Considering all of these potential issues, the correct answer encompasses the full range of consequences: all scenarios can occur if separate sampling is not appropriately addressed. This comprehensive approach to understanding the