  Boosting algorithms are frequently used in applied data science and in
research. To date, the distinction between boosting with either gradient
descent or second-order Newton updates is often not made in both applied and
methodological research, and it is thus implicitly assumed that the difference
is irrelevant. The goal of this article is to clarify this situation. In
particular, we present gradient and Newton boosting, as well as a hybrid
variant of the two, in a unified framework. We compare these boosting
algorithms with trees as base learners using various datasets and loss
functions. Our experiments show that Newton boosting outperforms gradient and
hybrid gradient-Newton boosting in terms of predictive accuracy on the majority
of datasets. We also present evidence that the reason for this is not faster
convergence of Newton boosting. In addition, we introduce a novel tuning
parameter for tree-based Newton boosting which is interpretable and important
for predictive accuracy.
