A data scientist trains a PROC HPFOREST model on a credit scoring dataset. The training AUC is 0.95 but validation AUC is 0.71. Which PROC HPFOREST option is most likely to close this gap without changing the dataset?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because the 24-point AUC gap between training and validation indicates overfitting. Decreasing MAXDEPTH= limits how deep each tree can grow (reducing complexity) and increasing LEAFSIZE= requires a minimum number of observations per leaf (smoothing predictions).
Full explanation below image
Full Explanation
B is correct because the 24-point AUC gap between training and validation indicates overfitting. Decreasing MAXDEPTH= limits how deep each tree can grow (reducing complexity) and increasing LEAFSIZE= requires a minimum number of observations per leaf (smoothing predictions). Both options constrain individual tree complexity without changing the data. A (more trees) reduces variance but the primary issue is tree depth, not ensemble size. C (more features per split) increases correlation between trees and reduces diversity, potentially worsening overfitting. D (different seed) produces a different but equally overfit model.