  Multi-target prediction (MTP) serves as an umbrella term for machine learning
tasks that concern the simultaneous prediction of multiple target variables.
Classical instantiations are multi-label classification, multivariate
regression, multi-task learning, dyadic prediction, zero-shot learning, network
inference, and matrix completion. Despite the significant similarities, all
these domains have evolved separately into distinct research areas over the
last two decades. This led to the development of a plethora of
highly-engineered methods, and created a substantially-high entrance barrier
for machine learning practitioners that are not experts in the field. In this
work we present a generic deep learning methodology that can be used for a wide
range of multi-target prediction problems. We introduce a flexible multi-branch
neural network architecture, partially configured via a questionnaire that
helps end-users to select a suitable MTP problem setting for their needs.
Experimental results for a wide range of domains illustrate that the proposed
methodology manifests a competitive performance compared to methods from
specific MTP domains.
