What Data Scientists Should Know About Multi-output and Multi-label Training

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Multi-output Machine Learning deals with complex decision-making in many real-world applications. Multi-task learning aims at learning multiple related tasks simultaneously, where each task outputs one single label, and learning multiple tasks is similar to learning multiple outputs. The first approach of training an inductive classifier or regression model can be a time-consuming task — particularly so when training data sets are very large. The second approach enables to create a model that simultaneously predicts a set of two or more classification labels, regression values, or even joint classification-regression outputs from only a single training iteration.