A classification task for the German credit data set. The aim is to predict creditworthiness, labeled as "good" and "bad". Positive class is set to label "good".

See example for the creation of a MeasureClassifCosts as described misclassification costs.


R6::R6Class inheriting from TaskClassif.


Data set originally published on UCI. This is the preprocessed version taken from package evtree.

Donor: Professor Dr. Hans Hofmann
Institut für Statistik und Ökonometrie
Universität Hamburg
FB Wirtschaftswissenschaften
Von-Melle-Park 5
2000 Hamburg 13



See also

Dictionary of Tasks: mlr_tasks

as.data.table(mlr_tasks) for a complete table of all (also dynamically created) Tasks.


task = tsk("german_credit") costs = matrix(c(0, 1, 5, 0), nrow = 2) dimnames(costs) = list(predicted = task$class_names, truth = task$class_names) measure = msr("classif.costs", id = "german_credit_costs", costs = costs) print(measure)
#> <MeasureClassifCosts:german_credit_costs> #> * Packages: - #> * Range: [0, Inf] #> * Minimize: TRUE #> * Properties: requires_task #> * Predict type: response