Datasets:
File size: 3,827 Bytes
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license: cc-by-4.0
pretty_name: ecoli
task_categories:
- tabular-classification
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- proteomics
- classification
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
---
# ecoli
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.
Five-class protein subcellular localization (327 proteins, five supplied sequence-derived features: mcg, gvh, lip, alm1, alm2). Retain PMLB classes cp, im, imU, om, pp; exclude the original imL, imS and omL classes. Omit the supervised aac discriminant because its training population is unverified, and remove constant chg. Protein IDs are never features. Use five stratified outer folds and three inner folds; fit imputation, encoding and scaling within training partitions.
| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Proteomics | classification | 327 | 5 | 5 |
**Splits.** 5 stored outer cross-validation folds (`data/fold_<i>/`), each with train/val/test files (fold 0: 228/33/66 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): `[]`. 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/ecoli")
```
A Croissant (1.1) description with Responsible-AI fields is distributed with the
TabBioMed release.
## Source and license
Source: PMLB (ecoli); verified against original UCI release
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}
}
```
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