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".

The default performance measure uses MeasureClassifCosts: Correct classifications have zero cost, while incorrectly predicting "good" yields a cost of 5 incorrectly predicting "bad" yields a cost of 1.

## Source

Data set originally published on UCI. This is the preprocessed version taken from package evtree.

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

## Usage

mlr_tasks\$get("german_credit")