mlr_tasks_german_credit: German Credit Classification Task

In mlr3: Machine Learning in R - Next Generation

German Credit Classification Task

Description

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.

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”

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

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

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

• Package mlr3data for more toy tasks.

• Package mlr3oml for downloading tasks from https://www.openml.org.

• Package 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).

• mlr3fselect and mlr3filters for feature selection and feature filtering.

• Extension packages for additional task types:

  • Unsupervised clustering: mlr3cluster

  • Probabilistic supervised regression and survival analysis: https://mlr3proba.mlr-org.com/.

Other Task: TaskClassif, TaskRegr, TaskSupervised, TaskUnsupervised, Task, mlr_tasks_boston_housing, 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, mlr_tasks

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)

mlr3 documentation built on Nov. 17, 2023, 5:07 p.m.