Multi-output Machine Learning — MixedRandomForest Introduction Multi-output learning subsumes many learning problems in multiple disciplines and deals with complex decision-making in many real-world applications. It has a multivariate nature and the multiple outputs may have complex interactions, architected to be handled by structured inference. The output values have diverse data types, depending on the type of ML problem. For example, 0/1 based Binary output values can refer to multi-label classification problem. Nominal output values to […]
