most citedMachine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?

9 citations · 10 across the 2 of their papers we have counts for

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2 papers

stat.ML2022★ 9 cited

Machine Learning for Multi-Output Regression: When should a holistic multivariate approach be preferred over separate univariate ones?

Lena Schmid, Alexander Gerharz, Andreas Groll +1

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…

stat.ML2021★ 1 cited

pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules

Michael Kirchhof, Lena Schmid, Christopher Reining +2

A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables applicatio…