A data scientist uses the gradient boosting gbtreetrain CAS action in SAS Viya and wants to tune the learning rate (shrinkage) parameter. What is the effect of decreasing the learning rate from 0.1 to 0.01 while keeping all other parameters constant?
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Short Explanation and Infographic
Here's the deal — b is correct because the learning rate (shrinkage) scales the contribution of each tree. A lower learning rate means each tree contributes less, requiring more boosting iterations (trees) to reach the same training loss.
Full explanation below image
Full Explanation
B is correct because the learning rate (shrinkage) scales the contribution of each tree. A lower learning rate means each tree contributes less, requiring more boosting iterations (trees) to reach the same training loss. However, this 'slower learning' often improves generalization by reducing the risk of overfitting to noise. A is wrong — slower learning requires more trees, not fewer. C is wrong because learning rate and MAXDEPTH are independent hyperparameters. D is wrong because feature importance is based on split gain contributions, not scaled by learning rate.