A data scientist runs PROC HPFOREST with SEED=12345 and obtains variable importance scores. The top variable is 'credit_score'. Which metric does PROC HPFOREST use by default to compute variable importance?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — a is correct because PROC HPFOREST computes variable importance as the average total decrease in the splitting criterion (Gini impurity for classification or variance reduction for regression) attributable to each variable across all trees. B (permutation importance) is computed by some implementations (e.g., scikit-learn) but is not the default in PROC HPFOREST.
Full explanation below image
Full Explanation
A is correct because PROC HPFOREST computes variable importance as the average total decrease in the splitting criterion (Gini impurity for classification or variance reduction for regression) attributable to each variable across all trees. B (permutation importance) is computed by some implementations (e.g., scikit-learn) but is not the default in PROC HPFOREST. C (Pearson correlation) is a bivariate statistic, not a tree-based importance measure. D is wrong because PROC HPFOREST does not embed LASSO.