  Gradient Boosting (GB) is a popular methodology used to solve prediction
problems by minimizing a differentiable loss function, $L$. GB performs very
well on tabular machine learning (ML) problems; however, as a pure ML solver it
lacks the ability to fit models with probabilistic but correlated
multi-dimensional outputs, for example, multiple correlated Bernoulli outputs.
GB also does not form intermediate abstract data embeddings, one property of
Deep Learning that gives greater flexibility and performance on other types of
problems. This paper presents a simple adjustment to GB motivated in part by
artificial neural networks. Specifically, our adjustment inserts a matrix
multiplication between the output of a GB model and the loss, $L$. This allows
the output of a GB model to have increased dimension prior to being fed into
the loss and is thus ``wider'' than standard GB implementations. We call our
method Wide Boosting (WB) and show that WB outperforms GB on mult-dimesional
output tasks and that the embeddings generated by WB contain are more useful in
downstream prediction tasks than GB output predictions alone.
