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 group (such as geographic region, year, etc), then how do we know if it is possible to train on one group, and predict accurately on another group? Cross-validation can be used to determine the extent to which this is possible, by first assigning fold IDs from 1 to K to all data (possibly using stratification, usually by group and label). Then we loop over test sets (group/fold combinations), train sets (same group, other groups, all groups), and compute test/prediction accuracy for each combination. Comparing test/prediction accuracy between same and other, we can determine the extent to which it is possible (perfect if same/other have similar test accuracy for each group; other is usually somewhat less accurate than same; other can be just as bad as featureless baseline when the groups have different patterns). For more information, <https://tdhock.github.io/blog/2023/R-gen-new-subsets/> describes the method in depth. How many train samples are required to get accurate predictions on a test set? Cross-validation can be used to answer this question, with variable size train sets.

install.packages("mlr3resampling")

Try the mlr3resampling package in your browser