TabBioMed camera-ready release

README.md

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+---
+license: cc-by-4.0
+pretty_name: thyroid
+task_categories:
+- tabular-classification
+tags:
+- tabular
+- biomedical
+- benchmark
+- TabBioMed
+- clinical-&-healthcare
+- classification
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train.parquet
+  - split: validation
+    path: data/val.parquet
+  - split: test
+    path: data/test.parquet
+---
+
+# thyroid
+
+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.
+
+Three-class UCI ANN thyroid task in TALENT row order. Released UCI feature values are restored before TALENT per-source-file z-scoring; the 15 binary flags are categorical. Standard preprocessing removes hypopituitary, constant in training, leaving 20 features (14 categorical). Labels and the stored TabBioMed 70/15/15 split are preserved. Any model scaling is fitted on its training partition.
+
+| Domain | Task | Samples | Features | Classes |
+|---|---|---|---|---|
+| Clinical & Healthcare | classification | 7,200 | 20 | 3 |
+
+**Splits.** Fixed train/val/test split (5040/1080/1080 rows).
+
+## 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): `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]`. 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.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("TabBioMed/thyroid")
+```
+
+A Croissant (1.1) description with Responsible-AI fields is distributed with the
+TabBioMed release.
+
+## Source and license
+
+Source: Talent Benchmark Dataset - thyroid
+
+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}
+}
+```

data/test.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d98f486509f0ab1f9c14fe33da90a5012c2ec62a536ea16c5453797b45148470
+size 32404

data/train.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b8b5713e84ad53387ba50c1d4097947ccda3f25a21ac872e05deb3414caecac3
+size 87080

data/val.parquet

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0ade98399373b9b5db712768a5d61ea30b96750c1dc35dcb0d5dd3ce8625894
+size 32372

feature_names.json

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+[
+  "age",
+  "sex",
+  "on_thyroxine",
+  "query_on_thyroxine",
+  "on_antithyroid_medication",
+  "sick",
+  "pregnant",
+  "thyroid_surgery",
+  "I131_treatment",
+  "query_hypothyroid",
+  "query_hyperthyroid",
+  "lithium",
+  "goitre",
+  "tumor",
+  "psych",
+  "TSH",
+  "T3",
+  "TT4",
+  "T4U",
+  "FTI"
+]

metadata.json

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+{
+  "READY": true,
+  "name": "thyroid",
+  "task": "classification",
+  "num_classes": 3,
+  "cat_idx": [
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14
+  ],
+  "num_samples": 7200,
+  "num_features": 20,
+  "imbalance_ratio": 40.1566,
+  "description": "Three-class UCI ANN thyroid task in TALENT row order. Released UCI feature values are restored before TALENT per-source-file z-scoring; the 15 binary flags are categorical. Standard preprocessing removes hypopituitary, constant in training, leaving 20 features (14 categorical). Labels and the stored TabBioMed 70/15/15 split are preserved. Any model scaling is fitted on its training partition.",
+  "source": "Talent Benchmark Dataset - thyroid",
+  "label": "clinical & healthcare",
+  "curation_protocol": "clinical_source_coding_v1"
+}