  Multiple Additive Regression Trees (MART), an ensemble model of boosted
regression trees, is known to deliver high prediction accuracy for diverse
tasks, and it is widely used in practice. However, it suffers an issue which we
call over-specialization, wherein trees added at later iterations tend to
impact the prediction of only a few instances, and make negligible contribution
towards the remaining instances. This negatively affects the performance of the
model on unseen data, and also makes the model over-sensitive to the
contributions of the few, initially added tress. We show that the commonly used
tool to address this issue, that of shrinkage, alleviates the problem only to a
certain extent and the fundamental issue of over-specialization still remains.
In this work, we explore a different approach to address the problem that of
employing dropouts, a tool that has been recently proposed in the context of
learning deep neural networks. We propose a novel way of employing dropouts in
MART, resulting in the DART algorithm. We evaluate DART on ranking, regression
and classification tasks, using large scale, publicly available datasets, and
show that DART outperforms MART in each of the tasks, with a significant
margin. We also show that DART overcomes the issue of over-specialization to a
considerable extent.
