  Two-part models and Tweedie generalized linear models (GLMs) have been used
to model loss costs for short-term insurance contract. For most portfolios of
insurance claims, there is typically a large proportion of zero claims that
leads to imbalances resulting in inferior prediction accuracy of these
traditional approaches. This article proposes the use of tree-based models with
a hybrid structure that involves a two-step algorithm as an alternative
approach to these traditional models. The first step is the construction of a
classification tree to build the probability model for frequency. In the second
step, we employ elastic net regression models at each terminal node from the
classification tree to build the distribution model for severity. This hybrid
structure captures the benefits of tuning hyperparameters at each step of the
algorithm; this allows for improved prediction accuracy and tuning can be
performed to meet specific business objectives. We examine and compare the
predictive performance of such a hybrid tree-based structure in relation to the
traditional Tweedie model using both real and synthetic datasets. Our empirical
results show that these hybrid tree-based models produce more accurate
predictions without the loss of intuitive interpretation.
