During a project I have been working a lot with the scikit learn and xgboost source code and I have been missing L2-regualzation in the scikit learn since my experience from xgboost is that including it gives preformance improvements. Their implimentation of this looks the following:

#Leaf calculation - param.h line 285
dw = -sum_grad / (sum_hess + p.reg_lambda);
#gain Calculation /imprunity_improvement - param.h line 250
gain= Sqr(sum_grad) / (sum_hess + p.reg_lambda);

Then there are some additional for l1 regualization, maximum improvement, etc. but the other code seems to be enogth given scikit learn hyperparameters. Full implimentation at https://github.com/dmlc/xgboost/blob/master/src/tree/param.h

I scikit learn the leaf calculation path seems to be handled by adding self.reg_lambda to all the denominators and adding it to the class. While the imprunity_improvement seems to handled by changing proxy_impurity_improvement and all classes in _criterion.pyx and adding an extra hyperparameter to it.

Does this seem reasonable or relevant?