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
Dictionary
This Task can be instantiated via the dictionary mlr_tasks or with the associated sugar function tsk():
Meta Information
Task type: “classif”
Dimensions: 1000x21
Properties: “twoclass”
Has Missings:
FALSETarget: “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.
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:
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
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>: Cost-sensitive Classification
#> * Packages: mlr3
#> * Range: [0, Inf]
#> * Minimize: TRUE
#> * Average: macro
#> * Parameters: normalize=TRUE
#> * Properties: -
#> * Predict type: response