  Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular
data, which produce state of the art results in many prediction tasks. Despite
its popularity, the GBM framework suffers from a fundamental flaw in its base
learners. Specifically, most implementations utilize decision trees that are
typically biased towards categorical variables with large cardinalities. The
effect of this bias was extensively studied over the years, mostly in terms of
predictive performance. In this work, we extend the scope and study the effect
of biased base learners on GBM feature importance (FI) measures. We show that
although these implementation demonstrate highly competitive predictive
performance, they still, surprisingly, suffer from bias in FI. By utilizing
cross-validated (CV) unbiased base learners, we fix this flaw at a relatively
low computational cost. We demonstrate the suggested framework in a variety of
synthetic and real-world setups, showing a significant improvement in all GBM
FI measures while maintaining relatively the same level of prediction accuracy.
