The prediction of multiple numeric outputs at the same time is called multi-target regression (MTR), and it has gained
attention during the last decades. This task is a challenging research topic in supervised learning because it poses additional
difficulties to traditional single-target regression (STR), and many real-world problems involve the prediction of multiple
targets at once. One of the most successful approaches to deal with MTR, although not the only one, consists in transforming
the problem in several STR problems, whose outputs will be combined building up the MTR output. In this paper, the
Rotation Forest ensemble method, previously proposed for single-label classification and single-target regression, is adapted
to MTR tasks and tested with several regressors and data sets. Our proposal rotates the input space in an efficient and novel
fashion, avoiding extra rotations forced by MTR problem decomposition. Four approaches for MTR are used: single-target
(ST), stacked-single target (SST), Ensembles of Regressor Chains (ERC), and Multi-target Regression via Quantization
(MRQ). For assessing the benefits of the proposal, a thorough experimentation with 28 MTR data sets and statistical tests
are used, concluding that Rotation Forest, adapted by means of these approaches, outperforms other popular ensembles,
such as Bagging and Random Forest.
