Multi-Target Prediction with Trees and Tree Ensembles

Increasingly often, we need to learn predictive models from big or complex data, which may comprise many examples and many input/output dimensions. When more than one target variable has to be predicted, we talk about multi-target prediction. Predictive modeling problems may also be complex in other ways, e.g., they may involve incompletely/partially labelled data, as in semi-supervised learning, or data placed in a network context.
The talk will first give an introduction to the different tasks of multi-target prediction, such as multi-target classification and regression, hierarchical versions thereof, and versions of the tasks that involve additional complexity (such as semi-supervised multi-target regression and network-based hierarchical multi-label classification). It will continue to present methods for solving such tasks, in particular predictive clustering trees and ensembles thereof. Finally, it will present example applications of multi-target prediction in the life sciences, focusing on predictive modeling in virtual compound screening for drug repurposing.