Package: mlr3resampling 2024.10.28

mlr3resampling: Resampling Algorithms for 'mlr3' Framework

A supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, <doi:10.48550/arXiv.2410.08643> can be used to answer these question, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set.

Authors:Toby Hocking [aut, cre], Michel Lang [ctb], Bernd Bischl [ctb], Jakob Richter [ctb], Patrick Schratz [ctb], Giuseppe Casalicchio [ctb], Stefan Coors [ctb], Quay Au [ctb], Martin Binder [ctb], Florian Pfisterer [ctb], Raphael Sonabend [ctb], Lennart Schneider [ctb], Marc Becker [ctb], Sebastian Fischer [ctb]