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german_credit_numeric

  • Description:

This dataset classifies people described by a set of attributes as good or bad credit risks. The version here is the "numeric" variant where categorical and ordered categorical attributes have been encoded as indicator and integer quantities respectively.

Split Examples
'train' 1,000
  • Feature structure:
FeaturesDict({
    'features': Tensor(shape=(24,), dtype=tf.int32),
    'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=2),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
features Tensor (24,) tf.int32
label ClassLabel tf.int64
  • Citation:
@misc{Dua:2019 ,
author = "Dua, Dheeru and Graff, Casey",
year = "2017",
title = "{UCI} Machine Learning Repository",
url = "http://archive.ics.uci.edu/ml",
institution = "University of California, Irvine, School of Information and Computer Sciences"
}