Web (w5a)

libsvm:w5a@1 · Registry 1.4.0. Default version in this registry.

The w5a binary classification variant of the Web dataset, distributed by LIBSVM with predefined training and test artifacts and 300 sparse features.

Dimensions and tasks

Task	Type	Split	Samples	Predictors
default	classification	test	39,861	300
default	classification	train	9,888	300

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:w5a@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:w5a@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:w5a@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:w5a@1'

Representation and variables

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

Component	Kind	Type	Shape
train_features	sparse_matrix	float64	9,888 × 300
train_response	vector	int64	9,888
test_features	sparse_matrix	float64	39,861 × 300
test_response	vector	int64	39,861

Preprocessing

Upstream

Status: unknown.

LIBSVM documents the feature count and training and test sizes but does not describe feature construction, sample selection, or the split selection procedure for these artifacts.

Evidence: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#w5a, https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ref.html#JP98a

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: w5a. Retrieved: 2026-10-26.

Original source

Authors: John C. Platt.

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 this variant by LIBSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ref.html#JP98a.

License

Unknown

The LIBSVM dataset page identifies Platt's JP98a reference as the source but states no license terms for these artifacts: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#w5a. The LIBSVM software license does not establish a license for the distributed dataset.

Artifacts

Total download: 3.3 MiB. Sizes describe the distributed files, including any compression.

Artifact	Format	Compression	Size	Distribution	Preservation
train	libsvm	none	674.6 KiB	upstream-only	unknown
test	libsvm	none	2.7 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.