XGboost and regularization
XGB uses the two kinds of regularization in both classification and regression; each leaf is a continuous score, these scores added together for the final prediction (of log-odds in the classification case), so penalizing the weights makes sense in either setting.
See also L1 &amp; L2 Regularization in Light GBM

But yes, some hyperparameters (scale_pos_weight) seem to be vestigial:
What does xgb&#39;s scale_pos_weight parameter do for regression?
https://discuss.xgboost.ai/t/scale-pos-weight-for-regression/218/10