mlr3 (version 0.10.0)

mlr_tasks_german_credit: 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.

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

Gr<U+00F6>mping 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

Dictionary of Tasks: mlr_tasks

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

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

Examples

# NOT RUN {
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)
# }