---
license: cc-by-4.0
pretty_name: lymphography
task_categories:
- tabular-classification
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- clinical-&-healthcare
- binary
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: fold_0_train
    path: data/fold_0/train.parquet
  - split: fold_0_validation
    path: data/fold_0/val.parquet
  - split: fold_0_test
    path: data/fold_0/test.parquet
  - split: fold_1_train
    path: data/fold_1/train.parquet
  - split: fold_1_validation
    path: data/fold_1/val.parquet
  - split: fold_1_test
    path: data/fold_1/test.parquet
  - split: fold_2_train
    path: data/fold_2/train.parquet
  - split: fold_2_validation
    path: data/fold_2/val.parquet
  - split: fold_2_test
    path: data/fold_2/test.parquet
  - split: fold_3_train
    path: data/fold_3/train.parquet
  - split: fold_3_validation
    path: data/fold_3/val.parquet
  - split: fold_3_test
    path: data/fold_3/test.parquet
  - split: fold_4_train
    path: data/fold_4/train.parquet
  - split: fold_4_validation
    path: data/fold_4/val.parquet
  - split: fold_4_test
    path: data/fold_4/test.parquet
---

# lymphography

Part of **TabBioMed** (*TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning*), a benchmark of 94 curated biomedical
tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.

Lymphography (OpenML ID 10), restricted to metastases (1; 81 samples) versus malignant lymph (0; 61 samples). The two normal and four fibrosis cases are excluded. All 18 source features and the source row order are retained. Evaluation uses five stratified outer folds and three stratified inner folds for hyperparameter selection; preprocessing is fitted within training partitions.

| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Clinical & Healthcare | binary | 142 | 18 | 2 |

**Splits.** 5 stored outer cross-validation folds (`data/fold_<i>/`), each with train/val/test files (fold 0: 98/15/29 rows). Every row appears in exactly one outer test fold. HPO in the paper uses inner folds drawn from each outer non-test pool.

## Files

- `data/**.parquet`: one row per sample. `row_idx` is the row index in the original
  TabBioMed array; `f_<i>[_<name>]` are the features (in order); `target` (or `target_<j>`
  for multi-target tasks) is the label. Categorical feature indices (0-based, among the
  features): `[0, 1, 2, 3, 4, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16]`. Missing values are stored as nulls.
- `metadata.json`: TabBioMed task metadata (task type, categorical indices, source, description).
- `feature_names.json`: original feature names, in column order.
- `sample_metadata.csv`: per-row provenance (identifiers, source annotations); not features.

```python
from datasets import load_dataset
ds = load_dataset("TabBioMed/lymphography")
```

A Croissant (1.1) description with Responsible-AI fields is distributed with the
TabBioMed release.

## Source and license

Source: OpenML_Dataset_ID_10

This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also
cite and respect the terms of the original source.

## Citation

```bibtex
@inproceedings{tabbiomed2026,
  title     = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
  author    = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}
```