  The calibration and training of a neural network is a complex and
time-consuming procedure that requires significant computational resources to
achieve satisfactory results. Key obstacles are a large number of
hyperparameters to select and the onset of overfitting in the face of a small
amount of data. In this framework, we propose an innovative training strategy
for feed-forward architectures - called split-boost - that improves performance
and automatically includes a regularizing behaviour without modeling it
explicitly. Such a novel approach ultimately allows us to avoid explicitly
modeling the regularization term, decreasing the total number of
hyperparameters and speeding up the tuning phase. The proposed strategy is
tested on a real-world (anonymized) dataset within a benchmark medical
insurance design problem.
