Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?. (arXiv:2201.05340v1 [stat.ML])

Tree-based ensembles such as the Random Forest are modern classics among
statistical learning methods. In particular, they are used for predicting
univariate responses. In case of multiple outputs the question arises whether
we separately fit univariate models or directly follow a multivariate approach.
For the latter, several possibilities exist that are, e.g. based on modified
splitting or stopping rules for multi-output regression. In this work we
compare these methods in extensive simulations to help in answering the primary
question when …

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