Description Usage Format Details Source Examples

This file concerns credit card applications of 690 households.

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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.

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.

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.

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