  Multi-output prediction deals with the prediction of several targets of
possibly diverse types. One way to address this problem is the so called
problem transformation method. This method is often used in multi-label
learning, but can also be used for multi-output prediction due to its
generality and simplicity. In this paper, we introduce an algorithm that uses
the problem transformation method for multi-output prediction, while
simultaneously learning the dependencies between target variables in a sparse
and interpretable manner. In a first step, predictions are obtained for each
target individually. Target dependencies are then learned via a component-wise
boosting approach. We compare our new method with similar approaches in a
benchmark using multi-label, multivariate regression and mixed-type datasets.
