In SAS, a data scientist wants to use cost-sensitive learning to handle a class imbalance problem in PROC HPLOGISTIC. Which approach correctly applies differential misclassification costs?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because PROC HPLOGISTIC supports a WEIGHT statement that applies observation-level weights. By assigning higher weights to minority class observations (e.g., weight = 1/class_proportion), the procedure effectively upweights the loss for misclassifying minority class observations, implementing cost-sensitive learning.
Full explanation below image
Full Explanation
A is correct because PROC HPLOGISTIC supports a WEIGHT statement that applies observation-level weights. By assigning higher weights to minority class observations (e.g., weight = 1/class_proportion), the procedure effectively upweights the loss for misclassifying minority class observations, implementing cost-sensitive learning. B is wrong because PROC HPLOGISTIC does not have a COST= option in the MODEL statement. C is wrong because PROC HPLOGISTIC does not have a BALANCE= option. D is wrong because PRIOR= adjusts posterior probability thresholds but is not a cost specification.