  Internet companies are facing the need for handling large-scale machine
learning applications on a daily basis and distributed implementation of
machine learning algorithms which can handle extra-large scale tasks with great
performance is widely needed. Deep forest is a recently proposed deep learning
framework which uses tree ensembles as its building blocks and it has achieved
highly competitive results on various domains of tasks. However, it has not
been tested on extremely large scale tasks. In this work, based on our
parameter server system, we developed the distributed version of deep forest.
To meet the need for real-world tasks, many improvements are introduced to the
original deep forest model, including MART (Multiple Additive Regression Tree)
as base learners for efficiency and effectiveness consideration, the cost-based
method for handling prevalent class-imbalanced data, MART based feature
selection for high dimension data and different evaluation metrics for
automatically determining of the cascade level. We tested the deep forest model
on an extra-large scale task, i.e., automatic detection of cash-out fraud, with
more than 100 millions of training samples. Experimental results showed that
the deep forest model has the best performance according to the evaluation
metrics from different perspectives even with very little effort for parameter
tuning. This model can block fraud transactions in a large amount of money each
day. Even compared with the best-deployed model, the deep forest model can
additionally bring into a significant decrease in economic loss each day.
