  Vehicle insurance claims size prediction needs methods to efficiently handle
these claims. Machine learning (ML) is one of the methods that solve this
problem. Tree-based ensemble learning algorithms are highly effective and
widely used ML methods. This study considers how vehicle insurance providers
incorporate ML methods in their companies and explores how the models can be
applied to insurance big data. We utilize various tree-based ML methods, such
as bagging, random forest, and gradient boosting, to determine the relative
importance of predictors in predicting claims size and to explore the
relationships between claims size and predictors. Furthermore, we evaluate and
compare these models' performances. The results show that tree-based ensemble
methods are better than the classical least square method.
  Keywords: claims size prediction; machine learning; tree-based ensemble
methods; vehicle insurance.
