  Tree ensembles distribute feature importance evenly amongst groups of
correlated features. The average feature ranking of the correlated group is
suppressed, which reduces interpretability and complicates feature selection.
In this paper we present ControlBurn, a feature selection algorithm that uses a
weighted LASSO-based feature selection method to prune unnecessary features
from tree ensembles, just as low-intensity fire reduces overgrown vegetation.
Like the linear LASSO, ControlBurn assigns all the feature importance of a
correlated group of features to a single feature. Moreover, the algorithm is
efficient and only requires a single training iteration to run, unlike
iterative wrapper-based feature selection methods. We show that ControlBurn
performs substantially better than feature selection methods with comparable
computational costs on datasets with correlated features.
