  Multi-target regression is concerned with the prediction of multiple
continuous target variables using a shared set of predictors. Two key
challenges in multi-target regression are: (a) modelling target dependencies
and (b) scalability to large output spaces. In this paper, a new multi-target
regression method is proposed that tries to jointly address these challenges
via a novel problem transformation approach. The proposed method, called MRQ,
is based on the idea of quantizing the output space in order to transform the
multiple continuous targets into one or more discrete ones. Learning on the
transformed output space naturally enables modeling of target dependencies
while the quantization strategy can be flexibly parameterized to control the
trade-off between prediction accuracy and computational efficiency. Experiments
on a large collection of benchmark datasets show that MRQ is both highly
scalable and also competitive with the state-of-the-art in terms of accuracy.
In particular, an ensemble version of MRQ obtains the best overall accuracy,
while being an order of magnitude faster than the runner up method.
