I am modeling a continuous regression/forecasting problem for very right-skewed data. I've been using ElasticNet and Huber regression with quite a bit of success, and have recently moved into using XGBoost to see if it'll provide any additional value. The dimensions of my training matrix is 60,000 rows by 500 columns.
What I've found is that the much simpler, more interpretable ElasticNet/Huber regression models very often outperform any XGBoost model I've built. The only way I can get XGBoost to compete is by using a ton of different forms of regularization. In particular: the most performant XGBoost models have had
reg_lambda parameters in the [10-150] range;
gamma in the
[25, 100] range,
subsample of 0.5,
colsample_by_tree of 0.5, and shallow
max_depths, e.g. 3/4/5, with around 150
From what I've gathered in various tutorials online,
gamma values over 10 or 20 seem to be very high, although I completely acknowledge that statement could be very dependent on the characteristics of the dataset being used.
For this super-regularized model, the predictions and feature importances make sense from an intuitive perspective.
I guess I'm just looking for some input – is it insane that I have such high regularization parameters, or am I more justified than once thought in these high values, since the proof seems to be in the pudding with the model's predictive power/generalizability and important features?