german: German credit scoring data

Description Usage Format Details Source References Examples

Description

See website for details of data attributes

Usage

1

Format

A data frame with 1000 observations on the following 21 variables.

V1

a factor with levels A11 A12 A13 A14

V2

a numeric vector

V3

a factor with levels A30 A31 A32 A33 A34

V4

a factor with levels A40 A41 A410 A42 A43 A44 A45 A46 A48 A49

V5

a numeric vector

V6

a factor with levels A61 A62 A63 A64 A65

V7

a factor with levels A71 A72 A73 A74 A75

V8

a numeric vector

V9

a factor with levels A91 A92 A93 A94

V10

a factor with levels A101 A102 A103

V11

a numeric vector

V12

a factor with levels A121 A122 A123 A124

V13

a numeric vector

V14

a factor with levels A141 A142 A143

V15

a factor with levels A151 A152 A153

V16

a numeric vector

V17

a factor with levels A171 A172 A173 A174

V18

a factor with levels good bad

V19

a factor with levels A191 A192

V20

a factor with levels A201 A202

V21

a 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/ml/index.php

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

1

Example output



gamclass documentation built on Nov. 14, 2020, 1:07 a.m.