Datasets:

TabBioMed
/

spectf

license: cc-by-4.0
pretty_name: spectf
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

spectf

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.

Normal (0; 55) versus abnormal (1; 212) cardiac SPECT, using 44 supplied rest/stress image summaries per patient. Remove 82 redundant PMLB copies; the 267 retained records exactly match the corrected UCI train/test union. Pool that cohort for benchmark CV rather than using the UCI 80/187 split. Use five stratified outer folds and three inner folds; fit imputation, encoding and scaling within training partitions.

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	267	44	2

Splits. 5 stored outer cross-validation folds (data/fold_<i>/), each with train/val/test files (fold 0: 186/27/54 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.

from datasets import load_dataset
ds = load_dataset("TabBioMed/spectf")

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

Source and license

Source: PMLB (spectf); 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

@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}
}