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.

• Homepage: https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data)

• Source code: tfds.structured.GermanCreditNumeric

• Versions:

  • 1.0.0 (default): No release notes.

• Download size: 99.61 KiB

• Dataset size: 58.61 KiB

• Auto-cached (documentation): Yes

• Splits:

Split	Examples
'train'	1,000

• Feature structure:

FeaturesDict({
    'features': Tensor(shape=(24,), dtype=int32),
    'label': ClassLabel(shape=(), dtype=int64, num_classes=2),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
features	Tensor	(24,)	int32
label	ClassLabel		int64

• Supervised keys (See as_supervised doc): ('features', 'label')

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• 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"
}