  In this work a novel, automated process for constructing and initializing
deep feed-forward neural networks based on decision trees is presented. The
proposed algorithm maps a collection of decision trees trained on the data into
a collection of initialized neural networks, with the structures of the
networks determined by the structures of the trees. The tree-informed
initialization acts as a warm-start to the neural network training process,
resulting in efficiently trained, accurate networks. These models, referred to
as "deep jointly-informed neural networks" (DJINN), demonstrate high predictive
performance for a variety of regression and classification datasets, and
display comparable performance to Bayesian hyper-parameter optimization at a
lower computational cost. By combining the user-friendly features of decision
tree models with the flexibility and scalability of deep neural networks, DJINN
is an attractive algorithm for training predictive models on a wide range of
complex datasets.
