A SAS developer builds a gradient boosting model using PROC TREEBOOST with the NTREES=200, SHRINKAGE=0.05, and SUBSAMPLE=0.8 options. What does SUBSAMPLE=0.8 control in stochastic gradient boosting?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because in stochastic gradient boosting, SUBSAMPLE= specifies the fraction of training rows randomly selected (without replacement) to fit each successive tree. With SUBSAMPLE=0.8, each tree is trained on 80% of training observations, introducing stochasticity that improves generalization and reduces overfitting.
Full explanation below image
Full Explanation
B is correct because in stochastic gradient boosting, SUBSAMPLE= specifies the fraction of training rows randomly selected (without replacement) to fit each successive tree. With SUBSAMPLE=0.8, each tree is trained on 80% of training observations, introducing stochasticity that improves generalization and reduces overfitting. A describes column (feature) subsampling. C (train/test split) is a data preparation step, not the SUBSAMPLE option. D is wrong; gradient computation subsampling is not what this parameter controls.