What Data Scientists should know about Multi-output and Multi-label Training

It has the 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, In Multi-output pattern recognition problems, each instance in the dataset have two or more output values (nominal or real-valued)-- i.e., the output value is a vector rather than a scalar. Here in this blog, we discuss about a Mixed/Multi-target RandomForest model, that supports multi-output problems with multiple classification outputs, multiple regression outputs, as well as arbitrary joint classification-regression outputs. Further the algorithm provides support for mixed-task multi-task learning, i.e., it is possible to train the model on any number of classification tasks and regression tasks, simultaneously. The Random Forest predictor lets each individual ensemble member vote for the most probable output according to its learned decision rule.