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.

Format

R6::R6Class inheriting from TaskClassif.

Source

Data set originally published on UCI. This is the preprocessed version taken from package rchallenge with factors instead of dummy variables, and corrected as proposed by Ulrike Grömping.

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

Construction

mlr_tasks$get("german_credit")
tsk("german_credit")

Meta Information

  • Task type: “classif”

  • Dimensions: 1000x21

  • Properties: “twoclass”

  • Has Missings: FALSE

  • Target: “credit_risk”

  • Features: “age”, “amount”, “credit_history”, “duration”, “employment_duration”, “foreign_worker”, “housing”, “installment_rate”, “job”, “number_credits”, “other_debtors”, “other_installment_plans”, “people_liable”, “personal_status_sex”, “present_residence”, “property”, “purpose”, “savings”, “status”, “telephone”

References

Grömping U (2019). “South German Credit Data: Correcting a Widely Used Data Set.” Reports in Mathematics, Physics and Chemistry 4, Department II, Beuth University of Applied Sciences Berlin. http://www1.beuth-hochschule.de/FB_II/reports/Report-2019-004.pdf.

See also

Examples

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