  Gradient boosting methods based on Structured Categorical Decision Trees
(SCDT) have been demonstrated to outperform numerical and one-hot-encodings on
problems where the categorical variable has a known underlying structure.
However, the enumeration procedure in the SCDT is infeasible except for
categorical variables with low or moderate cardinality. We propose and
implement two methods to overcome the computational obstacles and efficiently
perform Gradient Boosting on complex structured categorical variables. The
resulting package, called StructureBoost, is shown to outperform established
packages such as CatBoost and LightGBM on problems with categorical predictors
that contain sophisticated structure. Moreover, we demonstrate that
StructureBoost can make accurate predictions on unseen categorical values due
to its knowledge of the underlying structure.
