mlr_tasks_german_credit function

German Credit Classification Task

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 list("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

Dictionary

This Task can be instantiated via the dictionary mlr_tasks or with the associated sugar function tsk():

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

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)

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.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html

• Package list("mlr3data") for more toy tasks.

• Package list("mlr3oml") for downloading tasks from https://www.openml.org.

• Package list("mlr3viz") for some generic visualizations.

• Dictionary of Tasks : mlr_tasks

• as.data.table(mlr_tasks) for a table of available Tasks in the running session (depending on the loaded packages).

• list("mlr3fselect") and list("mlr3filters") for feature selection and feature filtering.

• Extension packages for additional task types:

  • Unsupervised clustering: list("mlr3cluster")
  • Probabilistic supervised regression and survival analysis: https://mlr3proba.mlr-org.com/.

Other Task: Task, TaskClassif, TaskRegr, TaskSupervised, TaskUnsupervised, california_housing, mlr_tasks, mlr_tasks_breast_cancer, mlr_tasks_iris, mlr_tasks_mtcars, mlr_tasks_penguins, mlr_tasks_pima, mlr_tasks_sonar, mlr_tasks_spam, mlr_tasks_wine, mlr_tasks_zoo