Datasets:

TabBioMed
/

pima_diabetes

row_idx int64	f_0_preg float64	f_1_plas float64	f_2_pres float64	f_3_skin float64	f_4_insu float64	f_5_mass float64	f_6_pedi float64	f_7_age float64	target int64
302	5	77	82	41	42	35.8	0.156	35	0
334	1	95	60	18	58	23.9	0.26	22	0
139	5	105	72	29	325	36.9	0.159	28	0
485	0	135	68	42	250	42.3	0.365	24	1
547	4	131	68	21	166	33.1	0.16	28	0
18	1	103	30	38	83	43.3	0.183	33	0
593	2	82	52	22	115	28.5	1.699	25	0
140	3	128	78	null	null	21.1	0.268	55	0
326	1	122	64	32	156	35.1	0.692	30	1
266	0	138	null	null	null	36.3	0.933	25	1
626	0	125	68	null	null	24.7	0.206	21	0
83	0	101	65	28	null	24.6	0.237	22	0
61	8	133	72	null	null	32.9	0.27	39	1
632	2	111	60	null	null	26.2	0.343	23	0
735	4	95	60	32	null	35.4	0.284	28	0
29	5	117	92	null	null	34.1	0.337	38	0
335	0	165	76	43	255	47.9	0.259	26	0
599	1	109	38	18	120	23.1	0.407	26	0
557	8	110	76	null	null	27.8	0.237	58	0
311	0	106	70	37	148	39.4	0.605	22	0
429	1	95	82	25	180	35	0.233	43	1
182	1	null	74	20	23	27.7	0.299	21	0
586	8	143	66	null	null	34.9	0.129	41	1
679	2	101	58	17	265	24.2	0.614	23	0
464	10	115	98	null	null	24	1.022	34	0
223	7	142	60	33	190	28.8	0.687	61	0
393	4	116	72	12	87	22.1	0.463	37	0
766	1	126	60	null	null	30.1	0.349	47	1
570	3	78	70	null	null	32.5	0.27	39	0
671	1	99	58	10	null	25.4	0.551	21	0
412	1	143	84	23	310	42.4	1.076	22	0
755	1	128	88	39	110	36.5	1.057	37	1
176	6	85	78	null	null	31.2	0.382	42	0
525	3	87	60	18	null	21.8	0.444	21	0
333	12	106	80	null	null	23.6	0.137	44	0
163	2	100	64	23	null	29.7	0.368	21	0
248	9	124	70	33	402	35.4	0.282	34	0
682	0	95	64	39	105	44.6	0.366	22	0
74	1	79	75	30	null	32	0.396	22	0
439	6	107	88	null	null	36.8	0.727	31	0
643	4	90	null	null	null	28	0.61	31	0
104	2	85	65	null	null	39.6	0.93	27	0
114	7	160	54	32	175	30.5	0.588	39	1
749	6	162	62	null	null	24.3	0.178	50	1
92	7	81	78	40	48	46.7	0.261	42	0
380	1	107	72	30	82	30.8	0.821	24	0
89	1	107	68	19	null	26.5	0.165	24	0
417	4	144	82	32	null	38.5	0.554	37	1
477	7	114	76	17	110	23.8	0.466	31	0
713	0	134	58	20	291	26.4	0.352	21	0
707	2	127	46	21	335	34.4	0.176	22	0
506	0	180	90	26	90	36.5	0.314	35	1
11	10	168	74	null	null	38	0.537	34	1
43	9	171	110	24	240	45.4	0.721	54	1
42	7	106	92	18	null	22.7	0.235	48	0
167	4	120	68	null	null	29.6	0.709	34	0
675	6	195	70	null	null	30.9	0.328	31	1
666	4	145	82	18	null	32.5	0.235	70	1
381	0	105	68	22	null	20	0.236	22	0
178	5	143	78	null	null	45	0.19	47	0
705	6	80	80	36	null	39.8	0.177	28	0
518	13	76	60	null	null	32.8	0.18	41	0
177	0	129	110	46	130	67.1	0.319	26	1
532	1	86	66	52	65	41.3	0.917	29	0
539	3	129	92	49	155	36.4	0.968	32	1
257	2	114	68	22	null	28.7	0.092	25	0
616	6	117	96	null	null	28.7	0.157	30	0
440	0	189	104	25	null	34.3	0.435	41	1
15	7	100	null	null	null	30	0.484	32	1
478	8	126	74	38	75	25.9	0.162	39	0
256	3	111	56	39	null	30.1	0.557	30	0
388	5	144	82	26	285	32	0.452	58	1
360	5	189	64	33	325	31.2	0.583	29	1
367	0	101	64	17	null	21	0.252	21	0
446	1	100	72	12	70	25.3	0.658	28	0
589	0	73	null	null	null	21.1	0.342	25	0
342	1	null	68	35	null	32	0.389	22	0
453	2	119	null	null	null	19.6	0.832	72	0
426	0	94	null	null	null	null	0.256	25	0
9	8	125	96	null	null	null	0.232	54	1
249	1	111	86	19	null	30.1	0.143	23	0
22	7	196	90	null	null	39.8	0.451	41	1
221	2	158	90	null	null	31.6	0.805	66	1
526	1	97	64	19	82	18.2	0.299	21	0
751	1	121	78	39	74	39	0.261	28	0
752	3	108	62	24	null	26	0.223	25	0
424	8	151	78	32	210	42.9	0.516	36	1
644	3	103	72	30	152	27.6	0.73	27	0
203	2	99	70	16	44	20.4	0.235	27	0
500	2	117	90	19	71	25.2	0.313	21	0
93	4	134	72	null	null	23.8	0.277	60	1
369	1	133	102	28	140	32.8	0.234	45	1
332	1	180	null	null	null	43.3	0.282	41	1
522	6	114	null	null	null	null	0.189	26	0
284	2	108	80	null	null	27	0.259	52	1
184	4	141	74	null	null	27.6	0.244	40	0
623	0	94	70	27	115	43.5	0.347	21	0
365	5	99	54	28	83	34	0.499	30	0
153	1	153	82	42	485	40.6	0.687	23	0
75	1	null	48	20	null	24.7	0.14	22	0

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pima_diabetes

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.

Pima diabetes classification (OpenML 37). Zero glucose, blood pressure, skinfold thickness, insulin and BMI denote missing measurements; zero pregnancies remain valid. Imputation is fitted during model training.

Domain	Task	Samples	Features	Classes
Clinical & Healthcare	binary	768	8	2

Splits. Fixed train/val/test split (538/115/115 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): []. 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.

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

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

Source and license

Source: OpenML_Dataset_ID_37

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}
}

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