AusCredit: Australian Credit Approval Dataset

In SVMMaj: Implementation of the SVM-Maj Algorithm

Australian Credit Approval Dataset

Description

This file concerns credit card applications of 690 households.

Format

This data set has been split into two components for the convenience of the model training.

data.frame-object X consists of with 6 numerical and 8 categorical attributes. The labels have been changed for the convenience of the statistical algorithms. For example, attribute 4 originally had 3 labels p,g,gg and these have been changed to labels 1,2,3.

Factor y indicates whether the application has been Accepted or Rejected

The training set AusCredit.tr contains a randomly selected set of 400 subjects, and AusCredit.te contains the remaining 290 subjects. AusCredit contains all 690 objects.

Details

All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.

This dataset is interesting because there is a good mix of attributes – continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values.

Source

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Examples

attach(AusCredit)
summary(X)
summary(y)
detach(AusCredit)

SVMMaj documentation built on May 23, 2022, 9:05 a.m.