german: German credit scoring data
In gamclass: Functions and Data for a Course on Modern Regression and Classification
german R Documentation
German credit scoring data
Description
See website for details of data attributes
Usage
german
Format
A data frame with 1000 observations on the following 21 variables.
V1a factor with levels A11 A12 A13 A14
V2a numeric vector
V3a factor with levels A30 A31 A32 A33 A34
V4a factor with levels A40 A41 A410 A42 A43 A44 A45 A46 A48 A49
V5a numeric vector
V6a factor with levels A61 A62 A63 A64 A65
V7a factor with levels A71 A72 A73 A74 A75
V8a numeric vector
V9a factor with levels A91 A92 A93 A94
V10a factor with levels A101 A102 A103
V11a numeric vector
V12a factor with levels A121 A122 A123 A124
V13a numeric vector
V14a factor with levels A141 A142 A143
V15a factor with levels A151 A152 A153
V16a numeric vector
V17a factor with levels A171 A172 A173 A174
V18a factor with levels good bad
V19a factor with levels A191 A192
V20a factor with levels A201 A202
V21a numeric vector
Details
700 good and 300 bad credits with 20 predictor variables. Data from 1973 to 1975. Stratified sample from actual credits with bad credits heavily oversampled. A cost matrix can be used.
Source
http://archive.ics.uci.edu/datasets
References
Grömping, U. (2019). South German Credit Data: Correcting a Widely
Used Data Set. Report 4/2019, Reports in Mathematics, Physics and
Chemistry, Department II, Beuth University of Applied Sciences Berlin.
Examples
data(german)
gamclass documentation built on Aug. 21, 2023, 5:07 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| german | R Documentation |
German credit scoring data
Description
See website for details of data attributes
Usage
german
Format
A data frame with 1000 observations on the following 21 variables.
V1a factor with levels
A11A12A13A14V2a numeric vector
V3a factor with levels
A30A31A32A33A34V4a factor with levels
A40A41A410A42A43A44A45A46A48A49V5a numeric vector
V6a factor with levels
A61A62A63A64A65V7a factor with levels
A71A72A73A74A75V8a numeric vector
V9a factor with levels
A91A92A93A94V10a factor with levels
A101A102A103V11a numeric vector
V12a factor with levels
A121A122A123A124V13a numeric vector
V14a factor with levels
A141A142A143V15a factor with levels
A151A152A153V16a numeric vector
V17a factor with levels
A171A172A173A174V18a factor with levels
goodbadV19a factor with levels
A191A192V20a factor with levels
A201A202V21a numeric vector
Details
700 good and 300 bad credits with 20 predictor variables. Data from 1973 to 1975. Stratified sample from actual credits with bad credits heavily oversampled. A cost matrix can be used.
Source
http://archive.ics.uci.edu/datasets
References
Grömping, U. (2019). South German Credit Data: Correcting a Widely Used Data Set. Report 4/2019, Reports in Mathematics, Physics and Chemistry, Department II, Beuth University of Applied Sciences Berlin.
Examples
data(german)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.