Adult (a5a)

libsvm:a5a@1 · Registry 1.4.0. Default version in this registry.

The a5a binary classification variant of the UCI Adult census income dataset, distributed by LIBSVM with predefined training and test artifacts. The task predicts whether annual income exceeds $50,000 using 123 binary features derived from the original 14 attributes.

Dimensions and tasks

Task	Type	Split	Samples	Predictors
default	classification	test	26,147	123
default	classification	train	6,414	123

Predictor counts follow each task's feature selection and exclude targets and identifiers. Sample counts are reported separately for each split.

Retrieve

These examples pin both the dataset version and the registry's exact index. An uncached dataset requires network access to its source. The CLI retrieves verified artifacts. The language clients also decode them.

Python

from datamonger import Registry, fetch_data

registry = Registry(
    release="1.4.0",
    index_sha256="ef83679266af9fcbb9ea194acdfb9bb6c380b440a574fc2ee7292899c5102909",
    index_url="https://github.com/jolars/datamonger/releases/download/registry-v1.4.0/index.json",
)
data = fetch_data("libsvm:a5a@1", registry=registry)

R

library(datamonger)

registry <- registry_selector(
  release = "1.4.0",
  index_sha256 = "ef83679266af9fcbb9ea194acdfb9bb6c380b440a574fc2ee7292899c5102909",
  index_url = "https://github.com/jolars/datamonger/releases/download/registry-v1.4.0/index.json"
)
data <- fetch_data("libsvm:a5a@1", registry = registry)

Julia

using Datamonger

registry = registry_selector(
    "1.4.0",
    "ef83679266af9fcbb9ea194acdfb9bb6c380b440a574fc2ee7292899c5102909",
    "https://github.com/jolars/datamonger/releases/download/registry-v1.4.0/index.json",
)
data = fetch_data("libsvm:a5a@1"; registry=registry)

CLI

cat > datamonger-registry.json <<'JSON'
{
  "index_sha256": "ef83679266af9fcbb9ea194acdfb9bb6c380b440a574fc2ee7292899c5102909",
  "index_url": "https://github.com/jolars/datamonger/releases/download/registry-v1.4.0/index.json",
  "release": "1.4.0",
  "schema_version": 1
}
JSON
datamonger --selector datamonger-registry.json fetch 'libsvm:a5a@1'

Representation and variables

Tabular data with a sparse representation. Decoder: libsvm-split version 1.

Component	Kind	Type	Shape
train_features	sparse_matrix	float64	6,414 × 123
train_response	vector	int64	6,414
test_features	sparse_matrix	float64	26,147 × 123
test_response	vector	int64	26,147

Preprocessing

Upstream

Status: documented.

LIBSVM documents 123 features and the training and test sizes. The quantile boundaries, fitting population, handling of original missing values, and split selection procedure have not been established.

feature-encoding. The six continuous Adult attributes are discretized into quantiles and represented by binary indicators. Each category of the eight categorical attributes is represented by a binary indicator.

Evidence: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#a5a, https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#a1a

Datamonger

Status: none.

Datamonger decodes the provider artifacts without additional statistical preprocessing.

Evidence: spec/decoders/libsvm-v1.md

Provenance and citation

Provider: LIBSVM. Upstream name: a5a. Retrieved: 2026-10-26.

Original source

Authors: Barry Becker, Ronny Kohavi.

Becker, B., and Kohavi, R. (1996). Adult [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5XW20.

Original Adult dataset, as cited by UCI.

John C. Platt. Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods - Support Vector Learning. MIT Press, 1998.

Reference JP98a associated with these variants by LIBSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ref.html#JP98a.

License

Unknown

UCI identifies Barry Becker and Ronny Kohavi as the Adult dataset creators and licenses the original dataset under CC BY 4.0: https://archive.ics.uci.edu/dataset/2/adult. The LIBSVM page identifies Adult as the source and documents its feature encoding but states no license terms for the transformed artifacts: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#a5a. The applicability of the original license to these distributed artifacts has not been established.

Artifacts

Total download: 2.2 MiB. Sizes describe the distributed files, including any compression.

Artifact	Format	Compression	Size	Distribution	Preservation
train	libsvm	none	448.1 KiB	upstream-only	unknown
test	libsvm	none	1.8 MiB	upstream-only	unknown

train: upstream

test: upstream

Versions in this registry

1 (default)

Registry record

Download the complete registry record, registry selector, or full registry index.

Index SHA-256: ef83679266af9fcbb9ea194acdfb9bb6c380b440a574fc2ee7292899c5102909.