If a company has an existing segmentation scheme variable, what is the best partition method to apply?

When dealing with an existing segmentation scheme, the stratify partition method is the most appropriate choice. Stratification involves dividing the dataset into distinct groups based on certain characteristics—in this case, the existing segmentation variable. This ensures that each segment is represented in both the training and test datasets proportionately. This method helps maintain the structure of the segments, allowing for better modeling that reflects the underlying patterns in the data.

Using stratification ensures that the model is trained with a balanced view of each segment, which is vital for robust performance and accurate insights. This is particularly important when the segmentation variable is crucial to the analysis or when certain segments are of specific interest to the business. In contrast, other methods like random partitioning can lead to an uneven distribution of segments, which may not accurately represent the population and can skew results.