Imbalance in Regression Datasets
Daniel Kowatsch Nicolas M. Müller Kilian Tscharke Philip Sperl Konstantin Bötinger
For classification, the problem of class imbalance is well known and has been
extensively studied. In this paper, we argue that imbalance in regression is an
equally important problem which has so far been overlooked: Due to under- and
over-representations in a data set's target distribution, regressors are prone
to degenerate to naive models, systematically neglecting uncommon training data
and over-representing targets seen often during training. We analyse this
problem theoretically and use resulting insights to develop a first definition
of imbalance in regression, which we show to be a generalisation of the
commonly employed imbalance measure in classification. With this, we hope to
turn the spotlight on the overlooked problem of imbalance in regression and to
provide common ground for future research.