  This paper presents a computationally efficient variant of gradient boosting
for multi-class classification and multi-output regression tasks. Standard
gradient boosting uses a 1-vs-all strategy for classifications tasks with more
than two classes. This strategy translates in that one tree per class and
iteration has to be trained. In this work, we propose the use of multi-output
regressors as base models to handle the multi-class problem as a single task.
In addition, the proposed modification allows the model to learn multi-output
regression problems. An extensive comparison with other multi-ouptut based
gradient boosting methods is carried out in terms of generalization and
computational efficiency. The proposed method showed the best trade-off between
generalization ability and training and predictions speeds.
