With a target event proportion of 2%, what sampling method should be used for a balanced 50/50 split in SAS EMiner?

To achieve a balanced 50/50 split in a dataset where the target event proportion is only 2%, using the stratified sampling method with an equal criterion is the best approach. This method allows for the preservation of the underlying distribution of the target variables while also ensuring that each class is equally represented in the sample.

When employing the stratified method with an equal criterion, each class (in this case, target event and non-event) is sampled in a way that ensures an equal number from both categories, regardless of the original proportions in the dataset. Therefore, you can obtain a balanced sample of cases, which is particularly important when the target event is rare, as it can improve model performance and provide a more accurate representation of both classes.

In contrast, other sampling methods may concentrate too heavily on the majority class or fail to provide the necessary representation from the minority class, potentially leading to biased or ineffective modeling. Stratifying with proportional sampling, for example, would not allow for the desired balance because it adjusts the sample size based on the original distribution, which does not support a 50/50 goal in a scenario where the target event is underrepresented.