Datasets:

TabBioMed
/

thyroid

row_idx int64	f_0_age float64	f_1_sex float64	f_2_on_thyroxine float64	f_3_query_on_thyroxine float64	f_4_on_antithyroid_medication float64	f_5_sick float64	f_6_pregnant float64	f_7_thyroid_surgery float64	f_8_I131_treatment float64	f_9_query_hypothyroid float64	f_10_query_hyperthyroid float64	f_11_lithium float64	f_13_tumor float64	f_14_psych float64	f_15_TSH float64	f_16_T3 float64	f_17_TT4 float64	f_18_T4U float64	f_19_FTI float64	target int64
0	0.69	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0059	0.018	0.163	0.109	0.149	2
1	0.6	0	0	0	0	0	0	0	0	0	0	0	0	0	0.027	0.018	0.065	0.099	0.066	1
5	0.35	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0012	0.0201	0.093	0.076	0.122	2
7	0.48	0	0	0	0	0	0	0	0	0	1	0	0	0	0.00232	0.0201	0.11329	0.096	0.11776	2
8	0.67	1	0	0	0	0	0	0	0	0	0	0	0	0	0.00007	0.0201	0.138	0.099	0.139	2
9	0.75	1	0	0	0	0	0	0	0	0	0	0	0	0	0.049	0.003	0.005	0.116	0.004	0
10	0.53	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00189	0.0206	0.11118	0.099	0.11207	2
14	0.74	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0018	0.017	0.107	0.084	0.127	2
17	0.17	0	1	0	0	0	0	0	0	0	0	0	0	0	0.015	0.018	0.132	0.089	0.148	2
18	0.54	0	0	1	0	1	0	0	0	0	0	0	0	0	0.00005	0.012	0.112	0.077	0.145	2
20	0.33	1	0	0	0	0	0	0	0	0	0	0	0	1	0.0003	0.025	0.107	0.092	0.116	2
22	0.31	0	0	0	0	0	0	1	0	0	0	0	0	0	0.00469	0.026	0.073	0.097	0.075	2
23	0.58	0	0	0	0	0	0	0	0	0	0	0	0	0	0.011	0.035	0.146	0.11	0.133	2
24	0.72	0	1	0	0	0	0	0	0	0	0	0	0	0	0.085	0.006	0.022	0.111	0.02	0
27	0.57	1	0	0	0	1	0	0	0	0	0	0	0	0	0.0002	0.02	0.091	0.116	0.077	2
28	0.46	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0001	0.007	0.069	0.077	0.089	2
29	0.71	1	1	0	0	0	0	0	0	0	0	0	0	0	0.0078	0.014	0.111	0.096	0.116	2
30	0.59	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0038	0.0201	0.182	0.15	0.121	2
31	0.65	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0018	0.026	0.121	0.104	0.115	2
34	0.35	1	0	0	0	0	0	0	0	0	1	0	0	0	0.00189	0.022	0.081	0.096	0.084	2
35	0.71	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00419	0.0201	0.112	0.089	0.126	2
36	0.69	1	0	0	0	0	0	0	0	0	0	0	0	0	0.00208	0.0201	0.102	0.066	0.155	2
37	0.75	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0026	0.0201	0.094	0.077	0.123	2
38	0.24	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0006	0.03	0.119	0.103	0.115	2
39	0.24	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0018	0.0201	0.164	0.134	0.122	2
42	0.6	1	0	0	0	0	0	0	0	0	0	0	0	0	0.00044	0.017	0.115	0.087	0.133	2
44	0.59	1	1	0	0	0	0	0	0	0	0	0	0	0	0.0052	0.0206	0.119	0.093	0.127	2
46	0.7	1	0	0	0	0	0	0	1	0	0	0	0	0	0.0023	0.028	0.09	0.11	0.082	2
48	0.6	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0003	0.018	0.114	0.1	0.114	2
49	0.72	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00189	0.0206	0.11118	0.099	0.11207	2
50	0.73	0	0	0	0	0	0	0	0	0	1	0	0	0	0.017	0.017	0.112	0.116	0.097	1
51	0.59	0	0	0	0	1	0	0	0	0	0	0	0	0	0.0071	0.019	0.098	0.111	0.088	1
52	0.58	0	0	0	0	0	0	0	0	0	0	0	1	0	0.059	0.019	0.038	0.113	0.034	0
53	0.53	1	0	0	0	0	0	0	0	0	0	0	0	0	0.001	0.0208	0.074	0.09	0.083	2
55	0.68	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0016	0.009	0.083	0.065	0.128	2
56	0.79	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0071	0.026	0.153	0.123	0.124	2
57	0.63	1	0	0	0	0	0	0	0	0	1	0	0	0	0.0029	0.0201	0.082	0.067	0.122	2
58	0.61	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0024	0.024	0.13	0.095	0.137	2
59	0.15	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00232	0.0201	0.11329	0.096	0.11776	2
60	0.6	0	0	0	0	0	0	0	0	1	0	0	0	0	0.00889	0.0174	0.075	0.097	0.077	1
61	0.61	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0025	0.0201	0.113	0.094	0.12	2
65	0.77	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0022	0.0201	0.072	0.075	0.096	2
66	0.54	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0024	0.022	0.116	0.103	0.112	2
67	0.53	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0009	0.0206	0.088	0.104	0.084	2
68	0.68	1	0	0	0	1	0	0	0	0	0	0	0	0	0.0067	0.014	0.101	0.1	0.10089	1
69	0.48	1	0	0	1	0	0	0	0	0	0	0	0	0	0.00021	0.038	0.168	0.098	0.171	2
70	0.45	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0063	0.02	0.119	0.104	0.114	1
72	0.76	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0031	0.012	0.1	0.07	0.143	2
73	0.31	0	0	0	0	0	0	0	0	0	0	0	0	0	0.001	0.022	0.124	0.099	0.125	2
74	0.39	1	0	0	0	0	0	0	0	0	0	1	0	1	0.0011	0.018	0.091	0.094	0.097	2
75	0.26	1	0	0	0	0	0	0	1	0	0	0	0	0	0.198	0.0096	0.005	0.095	0.0055	0
76	0.59	0	1	0	0	0	0	0	0	0	0	0	0	0	0.00003	0.02	0.164	0.092	0.178	2
77	0.83	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0028	0.011	0.066	0.071	0.093	2
79	0.74	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0017	0.014	0.075	0.074	0.101	2
80	0.6	0	0	0	0	0	0	0	0	0	0	0	0	1	0.0031	0.018	0.095	0.091	0.104	2
81	0.56	0	0	0	0	0	0	0	0	0	0	0	0	0	0.015	0.0208	0.133	0.097	0.137	1
82	0.23	0	0	0	0	0	0	0	0	0	0	0	0	1	0.0019	0.024	0.129	0.116	0.109	2
83	0.69	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0015	0.018	0.142	0.113	0.125	2
84	0.2	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0059	0.0201	0.114	0.131	0.087	2
86	0.68	0	0	0	0	0	0	0	0	0	0	0	0	0	0.001	0.068	0.109	0.096	0.113	2
88	0.2	1	0	0	0	0	0	0	0	0	0	0	0	0	0.00469	0.03	0.109	0.104	0.103	2
89	0.61	1	1	0	0	0	0	0	0	0	0	0	0	0	0.00025	0.025	0.101	0.1	0.101	2
92	0.61	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00469	0.011	0.089	0.075	0.118	2
93	0.52	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0045	0.012	0.108	0.073	0.148	2
94	0.37	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0019	0.0201	0.108	0.093	0.116	2
95	0.87	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0064	0.0005	0.04	0.068	0.059	0
96	0.74	0	0	0	0	1	0	0	0	0	0	0	0	0	0.003	0.013	0.096	0.101	0.095	2
97	0.48	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0031	0.025	0.09	0.103	0.088	2
98	0.52	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0017	0.024	0.115	0.121	0.095	2
99	0.47	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0011	0.0201	0.105	0.087	0.121	2
100	0.41	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0011	0.019	0.165	0.099	0.167	2
101	0.54	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0027	0.019	0.111	0.099	0.112	2
102	0.57	0	0	0	0	0	0	0	1	0	0	0	0	0	0.0001	0.024	0.081	0.089	0.091	2
104	0.3	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0002	0.019	0.086	0.09	0.096	2
105	0.58	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0014	0.025	0.099	0.115	0.086	2
106	0.38	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00232	0.027	0.166	0.149	0.111	2
107	0.34	0	0	0	1	0	1	0	0	0	1	0	0	0	0.00001	0.038	0.205	0.184	0.111	2
109	0.46	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0011	0.0206	0.07	0.058	0.122	2
110	0.73	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0013	0.015	0.102	0.087	0.117	2
111	0.5219	1	0	0	0	0	0	0	0	0	0	0	0	0	0.00038	0.02	0.113	0.108	0.104	2
112	0.42	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0019	0.0201	0.125	0.078	0.16	2
113	0.5	0	1	0	0	0	0	0	0	0	0	0	0	0	0.00066	0.0206	0.127	0.097	0.131	2
114	0.37	1	0	0	0	0	0	0	0	0	0	0	0	0	0.00208	0.023	0.093	0.082	0.113	2
115	0.61	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0036	0.0206	0.129	0.114	0.114	2
116	0.37	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0023	0.0201	0.126	0.116	0.107	2
117	0.7	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0007	0.009	0.073	0.099	0.07358	2
118	0.61	0	1	0	0	0	0	0	0	1	0	0	0	0	0.0068	0.009	0.096	0.101	0.095	2
120	0.51	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0001	0.0206	0.13	0.086	0.151	2
121	0.39	0	0	0	0	0	0	0	0	0	0	0	0	0	0.00081	0.0208	0.117	0.096	0.122	2
122	0.65	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0028	0.018	0.107	0.11	0.098	2
123	0.51	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0011	0.0201	0.108	0.099	0.11	2
124	0.33	0	0	0	0	0	0	0	0	0	1	0	0	0	0.0002	0.015	0.157	0.091	0.172	2
125	0.54	1	0	0	0	0	0	0	0	0	0	0	0	0	0.0034	0.017	0.113	0.093	0.122	2
126	0.67	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0027	0.019	0.138	0.095	0.145	2
128	0.51	1	1	0	0	0	0	0	0	0	0	0	0	0	0.00189	0.0206	0.071	0.087	0.082	2
129	0.64	0	0	0	0	0	0	0	0	0	0	1	0	0	0.0013	0.0201	0.125	0.094	0.133	2
130	0.6	0	1	0	0	0	0	0	0	0	0	0	0	0	0.0002	0.0206	0.159	0.107	0.149	2
133	0.72	1	0	0	0	0	0	0	0	0	1	0	1	0	0.0015	0.018	0.11118	0.093	0.112	2
134	0.68	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0001	0.0201	0.147	0.097	0.152	2
135	0.76	0	0	0	0	0	0	0	0	0	0	0	0	0	0.0022	0.0201	0.077	0.06	0.128	2

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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.

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

@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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