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penfever
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tunetables-results

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The dataset generation failed because of a cast error

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 29 new columns ({'TuneTables_Accuracy__test', '_zs-mutual_information-32_rdq_3000_mutual_information_Accuracy__test', '_zs-sparse_random_projection-32_sparse_random_projection_Accuracy__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly_bptt_128_Accuracy__test', '_zs-pca_white-32_rdq_3000_pca_white_Accuracy__test', '_zs-32_Accuracy__test', '_pt100-prop_Accuracy__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly-mutinf_bptt_128_Accuracy__test', '_ft_Accuracy__test', '_zs-sparse_random_projection-32_rdq_3000_sparse_random_projection_Accuracy__test', '_pt100_Accuracy__test', '_zs-random-32_bptt_128_random_Accuracy__test', '_pt10-5ens-randinit-avg-top1-highlr-longep_Accuracy__test', '_zs-random-32_rdq_3000_random_Accuracy__test', '_pt1000-10ens-randinit-avg-top2-unif-stopearly_bptt_128_Accuracy__test', '_pt100-uniform_bptt_128_Accuracy__test', '_zs-mutual_information-32_mutual_information_Accuracy__test', '_zs-isomap-32_rdq_3000_isomap_Accuracy__test', '_pt100-pca_Accuracy__test', '_pt100-rand_Accuracy__test', '_zs-random-32_random_Accuracy__test', '_zs-pca_white-32_pca_white_Accuracy__test', '_zs-ica-32_ica_Accuracy__test', '_zs-isomap-32_isomap_Accuracy__test', '_pt1000-10ens-randinit-avg-top2-unif_bptt_128_Accuracy__test', '_zs-ica-32_rdq_3000_ica_Accuracy__test', '_pt1000-uniform_bptt_128_Accuracy__test', '_pt1000-10ens-randinit-avg-top2_Accuracy__test', '_pt1000_Accuracy__test'}) and 29 missing columns ({'_zs-pca_white-32_rdq_3000_pca_white_AUC__test', '_pt1000-10ens-randinit-avg-top2_AUC__test', '_zs-isomap-32_isomap_AUC__test', '_zs-isomap-32_rdq_3000_isomap_AUC__test', '_pt1000-10ens-randinit-avg-top2-unif_bptt_128_AUC__test', '_pt1000-10ens-randinit-avg-top2-unif-stopearly_bptt_128_AUC__test', '_zs-32_AUC__test', '_pt100-rand_AUC__test', '_pt1000_AUC__test', '_pt1000-uniform_bptt_128_AUC__test', '_pt100-uniform_bptt_128_AUC__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly-mutinf_bptt_128_AUC__test', '_zs-ica-32_ica_AUC__test', '_zs-random-32_random_AUC__test', '_pt100-prop_AUC__test', '_zs-mutual_information-32_rdq_3000_mutual_information_AUC__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly_bptt_128_AUC__test', '_zs-sparse_random_projection-32_sparse_random_projection_AUC__test', '_pt10-5ens-randinit-avg-top1-highlr-longep_AUC__test', '_zs-mutual_information-32_mutual_information_AUC__test', '_zs-random-32_rdq_3000_random_AUC__test', '_zs-pca_white-32_pca_white_AUC__test', '_zs-random-32_bptt_128_random_AUC__test', '_pt100_AUC__test', '_ft_AUC__test', '_zs-sparse_random_projection-32_rdq_3000_sparse_random_projection_AUC__test', 'TuneTables_AUC__test', '_pt100-pca_AUC__test', '_zs-ica-32_rdq_3000_ica_AUC__test'}).

This happened while the csv dataset builder was generating data using

hf://datasets/penfever/tunetables-results/03_2024_tt_hard_main_plotting_data/main_plotting_data_Accuracy__test.csv (at revision 0e15323407a5526b8aabd3038c60f5c3f7f37d9a)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Unnamed: 0: int64
              time__train_x: double
              time__val_x: double
              time__test_x: double
              dataset_name: string
              CatBoost_Accuracy__test: double
              CatBoost_AUC__test: double
              CatBoost_F1__test: double
              CatBoost_Log Loss__test: double
              CatBoost__runtime: double
              time__train_y: double
              time__val_y: double
              time__test_y: double
              TabPFNs3000_Accuracy__test: double
              TabPFNs3000_AUC__test: double
              TabPFNs3000_F1__test: double
              Log Loss__test: double
              TabPFNs3000__runtime: double
              Classes: int64
              Features: int64
              Samples: int64
              Delta: double
              CatBoost__runtime_cumul: double
              TabPFNs3000__runtime_cumul: double
              _ft_Accuracy__test: double
              _pt100_Accuracy__test: double
              _pt100-pca_Accuracy__test: double
              _pt100-prop_Accuracy__test: double
              _pt100-rand_Accuracy__test: double
              _pt100-uniform_bptt_128_Accuracy__test: double
              _pt1000_Accuracy__test: double
              _pt1000-10ens-randinit-avg-top2_Accuracy__test: double
              _pt1000-10ens-randinit-avg-top2-unif_bptt_128_Accuracy__test: double
              _pt1000-uniform_bptt_128_Accuracy__test: double
              _zs-32_Accuracy__test: double
              _zs-ica-32_ica_Accuracy__test: double
              _zs-isomap-32_isomap_Accuracy__test: double
              _zs-mutual_information-32_mutual_information_Accuracy__test: double
              _zs-pca_white-32_pca_white_Accuracy__test: double
              _zs-random-32_random_Accuracy__test: double
              _zs-random-32_rdq_3000_random_Accuracy__test: double
              _zs-sparse_random_projection-32_sparse_random_projection_Accuracy__test: double
              _zs-ica-32_rdq_3000_ica_Accuracy__test: double
              _zs-isomap-32_rdq_3000_isomap_Accuracy__test: double
              _zs-mutual_information-32_rdq_3000_mutual_information_Accuracy__test: double
              _zs-pca_white-32_rdq_3000_pca_white_Accuracy__test: double
              _zs-sparse_random_projection-32_rdq_3000_sparse_random_projection_Accuracy__test: double
              _pt1000-10ens-randinit-avg-top1-unif-stopearly-mutinf_bptt_128_Accuracy__test: double
              _zs-random-32_bptt_128_random_Accuracy__test: double
              _pt1000-10ens-randinit-avg-top1-unif-stopearly_bptt_128_Accuracy__test: double
              _pt1000-10ens-randinit-avg-top2-unif-stopearly_bptt_128_Accuracy__test: double
              _pt10-5ens-randinit-avg-top1-highlr-longep_Accuracy__test: double
              TuneTables_Accuracy__test: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 8892
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'time__train_x': Value(dtype='float64', id=None), 'time__val_x': Value(dtype='float64', id=None), 'time__test_x': Value(dtype='float64', id=None), 'dataset_name': Value(dtype='string', id=None), 'CatBoost_Accuracy__test': Value(dtype='float64', id=None), 'CatBoost_AUC__test': Value(dtype='float64', id=None), 'CatBoost_F1__test': Value(dtype='float64', id=None), 'CatBoost_Log Loss__test': Value(dtype='float64', id=None), 'CatBoost__runtime': Value(dtype='float64', id=None), 'time__train_y': Value(dtype='float64', id=None), 'time__val_y': Value(dtype='float64', id=None), 'time__test_y': Value(dtype='float64', id=None), 'TabPFNs3000_Accuracy__test': Value(dtype='float64', id=None), 'TabPFNs3000_AUC__test': Value(dtype='float64', id=None), 'TabPFNs3000_F1__test': Value(dtype='float64', id=None), 'Log Loss__test': Value(dtype='float64', id=None), 'TabPFNs3000__runtime': Value(dtype='float64', id=None), 'Classes': Value(dtype='int64', id=None), 'Features': Value(dtype='int64', id=None), 'Samples': Value(dtype='int64', id=None), 'Delta': Value(dtype='float64', id=None), 'CatBoost__runtime_cumul': Value(dtype='float64', id=None), 'TabPFNs3000__runtime_cumul': Value(dtype='float64', id=None), '_ft_AUC__test': Value(dtype='float64', id=None), '_pt100_AUC__test': Value(dtype='float64', id=None), '_pt100-pca_AUC__test': Value(dtype='float64', id=None), '_pt100-prop_AUC__test': Value(dtype='float64', id=None), '_pt100-rand_AUC__test': Value(dt
              ...
              ica_AUC__test': Value(dtype='float64', id=None), '_zs-isomap-32_isomap_AUC__test': Value(dtype='float64', id=None), '_zs-mutual_information-32_mutual_information_AUC__test': Value(dtype='float64', id=None), '_zs-pca_white-32_pca_white_AUC__test': Value(dtype='float64', id=None), '_zs-random-32_random_AUC__test': Value(dtype='float64', id=None), '_zs-random-32_rdq_3000_random_AUC__test': Value(dtype='float64', id=None), '_zs-sparse_random_projection-32_sparse_random_projection_AUC__test': Value(dtype='float64', id=None), '_zs-ica-32_rdq_3000_ica_AUC__test': Value(dtype='float64', id=None), '_zs-isomap-32_rdq_3000_isomap_AUC__test': Value(dtype='float64', id=None), '_zs-mutual_information-32_rdq_3000_mutual_information_AUC__test': Value(dtype='float64', id=None), '_zs-pca_white-32_rdq_3000_pca_white_AUC__test': Value(dtype='float64', id=None), '_zs-sparse_random_projection-32_rdq_3000_sparse_random_projection_AUC__test': Value(dtype='float64', id=None), '_pt1000-10ens-randinit-avg-top1-unif-stopearly-mutinf_bptt_128_AUC__test': Value(dtype='float64', id=None), '_zs-random-32_bptt_128_random_AUC__test': Value(dtype='float64', id=None), '_pt1000-10ens-randinit-avg-top1-unif-stopearly_bptt_128_AUC__test': Value(dtype='float64', id=None), '_pt1000-10ens-randinit-avg-top2-unif-stopearly_bptt_128_AUC__test': Value(dtype='float64', id=None), '_pt10-5ens-randinit-avg-top1-highlr-longep_AUC__test': Value(dtype='float64', id=None), 'TuneTables_AUC__test': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1438, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1050, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 29 new columns ({'TuneTables_Accuracy__test', '_zs-mutual_information-32_rdq_3000_mutual_information_Accuracy__test', '_zs-sparse_random_projection-32_sparse_random_projection_Accuracy__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly_bptt_128_Accuracy__test', '_zs-pca_white-32_rdq_3000_pca_white_Accuracy__test', '_zs-32_Accuracy__test', '_pt100-prop_Accuracy__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly-mutinf_bptt_128_Accuracy__test', '_ft_Accuracy__test', '_zs-sparse_random_projection-32_rdq_3000_sparse_random_projection_Accuracy__test', '_pt100_Accuracy__test', '_zs-random-32_bptt_128_random_Accuracy__test', '_pt10-5ens-randinit-avg-top1-highlr-longep_Accuracy__test', '_zs-random-32_rdq_3000_random_Accuracy__test', '_pt1000-10ens-randinit-avg-top2-unif-stopearly_bptt_128_Accuracy__test', '_pt100-uniform_bptt_128_Accuracy__test', '_zs-mutual_information-32_mutual_information_Accuracy__test', '_zs-isomap-32_rdq_3000_isomap_Accuracy__test', '_pt100-pca_Accuracy__test', '_pt100-rand_Accuracy__test', '_zs-random-32_random_Accuracy__test', '_zs-pca_white-32_pca_white_Accuracy__test', '_zs-ica-32_ica_Accuracy__test', '_zs-isomap-32_isomap_Accuracy__test', '_pt1000-10ens-randinit-avg-top2-unif_bptt_128_Accuracy__test', '_zs-ica-32_rdq_3000_ica_Accuracy__test', '_pt1000-uniform_bptt_128_Accuracy__test', '_pt1000-10ens-randinit-avg-top2_Accuracy__test', '_pt1000_Accuracy__test'}) and 29 missing columns ({'_zs-pca_white-32_rdq_3000_pca_white_AUC__test', '_pt1000-10ens-randinit-avg-top2_AUC__test', '_zs-isomap-32_isomap_AUC__test', '_zs-isomap-32_rdq_3000_isomap_AUC__test', '_pt1000-10ens-randinit-avg-top2-unif_bptt_128_AUC__test', '_pt1000-10ens-randinit-avg-top2-unif-stopearly_bptt_128_AUC__test', '_zs-32_AUC__test', '_pt100-rand_AUC__test', '_pt1000_AUC__test', '_pt1000-uniform_bptt_128_AUC__test', '_pt100-uniform_bptt_128_AUC__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly-mutinf_bptt_128_AUC__test', '_zs-ica-32_ica_AUC__test', '_zs-random-32_random_AUC__test', '_pt100-prop_AUC__test', '_zs-mutual_information-32_rdq_3000_mutual_information_AUC__test', '_pt1000-10ens-randinit-avg-top1-unif-stopearly_bptt_128_AUC__test', '_zs-sparse_random_projection-32_sparse_random_projection_AUC__test', '_pt10-5ens-randinit-avg-top1-highlr-longep_AUC__test', '_zs-mutual_information-32_mutual_information_AUC__test', '_zs-random-32_rdq_3000_random_AUC__test', '_zs-pca_white-32_pca_white_AUC__test', '_zs-random-32_bptt_128_random_AUC__test', '_pt100_AUC__test', '_ft_AUC__test', '_zs-sparse_random_projection-32_rdq_3000_sparse_random_projection_AUC__test', 'TuneTables_AUC__test', '_pt100-pca_AUC__test', '_zs-ica-32_rdq_3000_ica_AUC__test'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/penfever/tunetables-results/03_2024_tt_hard_main_plotting_data/main_plotting_data_Accuracy__test.csv (at revision 0e15323407a5526b8aabd3038c60f5c3f7f37d9a)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Unnamed: 0 int64	time__train_x float64	time__val_x float64	time__test_x float64	dataset_name string	CatBoost_Accuracy__test float64	CatBoost_AUC__test float64	CatBoost_F1__test float64	CatBoost_Log Loss__test float64	CatBoost__runtime float64	time__train_y float64	time__val_y float64	time__test_y float64	TabPFNs3000_Accuracy__test float64	TabPFNs3000_AUC__test float64	TabPFNs3000_F1__test float64	Log Loss__test float64	TabPFNs3000__runtime float64	Classes int64	Features int64	Samples int64	Delta float64	CatBoost__runtime_cumul float64	TabPFNs3000__runtime_cumul float64	_ft_AUC__test float64	_pt100_AUC__test float64	_pt100-pca_AUC__test float64	_pt100-prop_AUC__test float64	_pt100-rand_AUC__test float64	_pt100-uniform_bptt_128_AUC__test float64	_pt1000_AUC__test float64	_pt1000-10ens-randinit-avg-top2_AUC__test float64	_pt1000-10ens-randinit-avg-top2-unif_bptt_128_AUC__test float64	_pt1000-uniform_bptt_128_AUC__test float64	_zs-32_AUC__test float64	_zs-ica-32_ica_AUC__test float64	_zs-isomap-32_isomap_AUC__test float64	_zs-mutual_information-32_mutual_information_AUC__test float64	_zs-pca_white-32_pca_white_AUC__test float64	_zs-random-32_random_AUC__test float64	_zs-random-32_rdq_3000_random_AUC__test float64	_zs-sparse_random_projection-32_sparse_random_projection_AUC__test float64	_zs-ica-32_rdq_3000_ica_AUC__test float64	_zs-isomap-32_rdq_3000_isomap_AUC__test float64	_zs-mutual_information-32_rdq_3000_mutual_information_AUC__test float64	_zs-pca_white-32_rdq_3000_pca_white_AUC__test float64	_zs-sparse_random_projection-32_rdq_3000_sparse_random_projection_AUC__test float64	_pt1000-10ens-randinit-avg-top1-unif-stopearly-mutinf_bptt_128_AUC__test float64	_zs-random-32_bptt_128_random_AUC__test float64	_pt1000-10ens-randinit-avg-top1-unif-stopearly_bptt_128_AUC__test float64	_pt1000-10ens-randinit-avg-top2-unif-stopearly_bptt_128_AUC__test float64	_pt10-5ens-randinit-avg-top1-highlr-longep_AUC__test float64	TuneTables_AUC__test float64
0	5.470622	0.308169	0.269775	openml__Agrawal1__146093	0.95	0.992252	0.95028	0.101591	6.048565	0.025716	216.104408	216.100852	0.946	0.991382	0.94613	0.118413	432.230976	2	9	1,000,000	0.004	142.218803	5,158.457872	0.973	0.976	0.976	0.976	0.976	0.992	0.992	0	0	0.992	0.99	0.99	0.99	0.99	0.99	0.99	0.992	0.99	0	0	0	0	0	0	0	0	0	0	0
1	6.712468	0.333047	0.279522	openml__BNG(labor)__2137	0.971	0.995435	0.9709	0.078912	7.325036	0.040458	229.750833	230.023553	0.938	0.982161	0.93836	0.15832	459.814844	2	16	1,000,000	0.033	226.240902	5,517.778131	0.978	0.994	0	0	0	0.993	0.993	0	0	0.993	0.977	0.977	0.977	0.977	0.977	0.977	0.982	0.977	0.982	0.982	0.982	0.982	0.982	0	0	0	0	0	0
2	1.588888	0.0934	0.089485	openml__BNG(vote)__212	0.975	0.996548	0.975359	0.069077	1.771773	0.01816	32.898037	32.942842	0.968	0.994736	0.967958	0.08944	65.859038	2	16	131,072	0.007	42.335094	1,975.77114	0.993	0.996	0	0	0	0.996	0.996	0	0	0.996	0.993	0.993	0.993	0.993	0.993	0.993	0.994	0.993	0.994	0.994	0.994	0.994	0.994	0	0	0	0	0	0
3	7.746181	0.024661	0.039858	openml__Bioresponse__9910	0.803	0.878566	0.802667	0.441356	7.8107	15.971731	2.510121	2.535231	0.819	0.885325	0.818667	0.426595	21.017083	2	1,776	3,751	-0.016	112.880729	637.68252	0.818	0.824	0	0	0	0.818	0.812	0	0	0.82	0.523	0.831	0.74	0.857	0.835	0.523	0.523	0.824	0.843	0.778	0.867	0.851	0.842	0	0	0	0	0	0.871
4	3.756384	0.20875	0.17901	openml__Click_prediction_small__7294	0.842	0.74119	0.842075	0.393111	4.144144	0.004192	205.156692	205.236468	0.834	0.660425	0.83419	0.438609	410.397352	2	11	1,997,410	0.008	84.526018	10,420.932465	0.595	0.658	0	0	0	0.668	0.667	0	0	0.674	0.656	0.656	0.656	0.656	0.656	0.656	0.664	0.656	0.664	0.664	0.664	0.664	0.664	0	0	0	0	0	0
5	7.002172	0.165818	0.166777	openml__airlines__189354	0.664	0.715663	0.663842	0.610232	7.334767	0.107882	46.354915	46.322323	0.602	0.627777	0.601505	0.662777	92.785119	2	7	539,383	0.062	154.948461	2,601.905863	0.622	0.663	0	0	0	0.697	0.685	0	0	0.687	0.613	0.613	0.613	0.613	0.613	0.613	0.62	0.613	0	0	0	0	0	0	0	0	0	0	0
6	32.919919	1.06518	1.028028	openml__albert__189356	0.702	0.772683	0.701651	0.570534	35.013126	0.290836	61.830542	61.859957	0.637	0.686705	0.637146	0.642012	123.981335	2	78	425,240	0.065	1,077.832536	2,363.267322	0.64	0.696	0	0	0	0.704	0.691	0	0	0.703	0.679	0.679	0.679	0.679	0.679	0.679	0.691	0.679	0	0	0	0	0	0	0	0	0	0	0
7	2.096973	0.004232	0.003759	openml__balance-scale__11	0.871	0.92697	0.876643	0.315156	2.104964	0.003427	0.483712	0.480451	0.984	0.998659	0.984106	0.037652	0.967589	3	4	625	-0.113	52.771632	28.858381	0.992	0.985	0	0	0	0.975	0.99	0.984	0.971	0.987	1	1	1	1	1	1	1	1	1	1	1	1	1	0.98	0	0	0	0	1
8	0.524348	0.002498	0.001858	openml__blood-transfusion-service-center__10101	0.76	0.683723	0.76	0.529754	0.528704	0.0027	0.411178	0.408556	0.8	0.742203	0.8	0.48331	0.822434	2	4	748	-0.04	24.005331	24.855342	0.774	0.636	0	0	0	0.711	0.749	0	0	0.753	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0.775	0	0	0	0	0	0.774
9	0.683357	0.002084	0.003628	openml__breast-cancer__145799	0.724	0.572222	0.724138	0.623272	0.689069	0.003648	0.491073	0.489	0.724	0.683333	0.724138	0.607276	0.983721	2	9	286	0	36.982686	28.809068	0.586	0.651	0	0	0	0.653	0.63	0	0	0.499	0.722	0.722	0.722	0.722	0.722	0.722	0.722	0.722	0.722	0.722	0.722	0.722	0.722	0.679	0	0	0	0	0.724
10	7.534318	0.122489	0.088212	openml__christine__168908	0.747	0.80579	0.747232	0.53844	7.745018	17.262028	2.645428	2.649614	0.718	0.803461	0.717712	0.543561	22.557071	2	1,636	5,418	0.029	331.16304	665.780806	0.754	0.776	0	0	0	0.819	0.764	0	0	0.795	0.65	0.761	0.719	0.803	0.766	0.65	0.646	0.735	0.788	0.747	0.821	0.791	0.751	0	0	0	0	0	0.827
11	0.710183	0.003037	0.001993	openml__climate-model-simulation-crashes__146819	0.931	0.919959	0.930864	0.181723	0.715213	0.003153	0.962894	0.960936	0.963	0.995918	0.962963	0.095939	1.926983	2	18	540	-0.032	24.901495	58.653851	0.923	0.82	0	0	0	0.942	0.863	0	0	0.86	0.908	0.908	0.908	0.908	0.908	0.908	0.908	0.908	0.908	0.908	0.908	0.908	0.908	0	0	0	0	0	0.912
12	1.767558	0.00417	0.003884	openml__cmc__23	0.541	0.724065	0.539462	0.905065	1.775612	0.007683	1.481638	1.517026	0.612	0.801547	0.610603	0.8145	3.006348	3	9	1,473	-0.071	78.094024	90.992666	0.721	0.714	0	0	0	0.704	0.726	0.474	0.468	0.715	0.725	0.725	0.725	0.725	0.725	0.725	0.725	0.725	0.725	0.725	0.725	0.725	0.725	0.722	0	0.722	0.709	0	0.731
13	3.11665	0.007943	0.008792	openml__colic__27	0.88	0.913657	0.88003	0.327921	3.133385	0.006265	1.039177	0.295073	0.878	0.925725	0.878378	0.339482	1.340514	2	22	368	0.002	218.034728	39.318812	0.919	0.913	0	0	0	0.896	0.918	0	0	0.916	0.932	0.932	0.932	0.932	0.932	0.932	0.932	0.932	0.932	0.932	0.932	0.932	0.932	0.912	0	0	0	0	0.93
14	16.109806	0.166454	0.075367	openml__connect-4__146195	0.821	0.871428	0.793513	0.473594	16.351627	0.078216	11.57487	11.56509	0.674	0.632796	0.561727	0.798451	23.218176	3	42	67,557	0.147	203.916567	696.121908	0.416	0.564	0	0	0	0.901	0.898	0	0	0.892	0.639	0.639	0.639	0.639	0.639	0.639	0.671	0.639	0.671	0.671	0.671	0.671	0.671	0	0	0	0	0	0
15	2.200719	0.00686	0.006669	openml__cylinder-bands__14954	0.787	0.875877	0.787037	0.443283	2.214247	0.00864	1.239279	0.138438	0.796	0.87798	0.796296	0.426672	1.386357	2	37	540	-0.009	93.963842	40.877432	0.933	0.923	0	0	0	0.864	0.929	0.911	0	0.888	0.938	0.938	0.938	0.938	0.938	0.938	0.938	0.938	0.938	0.938	0.938	0.938	0.938	0	0	0	0	0	0.936
16	12.705649	0.025386	0.024051	openml__dilbert__168909	0.962	0.998074	0.962014	0.189144	12.755086	20.246264	2.638321	2.643936	0.926	0.99487	0.926029	0.207929	25.52852	5	2,000	10,000	0.036	808.730207	761.177293	0.972	0.998	0.654	0.655	0.394	0.999	0.99	0	0	0.995	0.444	0.976	0.981	0.977	0.977	0.444	0.415	0.975	0.986	0.988	0.99	0.985	0.987	0	0	0	0	0	0
17	1.061346	0.002149	0.001866	openml__dresses-sales__125920	0.54	0.533662	0.54	0.737212	1.065361	0.004738	0.677673	0.674113	0.6	0.518883	0.6	0.680896	1.356524	2	12	500	-0.06	40.149266	40.880028	0.474	0.535	0	0	0	0.611	0.476	0	0	0.41	0.575	0.575	0.575	0.575	0.575	0.575	0.575	0.575	0.575	0.575	0.575	0.575	0.575	0	0	0	0	0	0.571
18	1.213773	0.004095	0.001866	openml__ecoli__145977	0.939	0.986417	0.940892	0.473745	1.219734	0.003157	0.520286	0.517488	0.727	0.933968	0.722274	0.808689	1.040931	8	7	336	0.212	73.058134	30.157485	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.934
19	0.785582	0.009653	0.009447	openml__eeg-eye-state__14951	0.892	0.959227	0.891856	0.290974	0.804682	0.009589	2.881329	2.88532	0.937	0.985335	0.93725	0.163974	5.776238	2	14	14,980	-0.045	27.734729	177.537891	0.606	0.653	0	0	0	0.673	0.743	0	0	0.704	0.974	0.974	0.974	0.974	0.974	0.974	0.985	0.974	0.985	0.985	0.985	0.985	0.985	0	0	0	0	0	0
20	2.590795	0.002214	0.003144	openml__elevators__3711	0.892	0.942216	0.891566	0.267698	2.596153	0.011596	3.048264	3.024167	0.895	0.954868	0.895181	0.235062	6.084026	2	18	16,599	-0.003	175.790243	186.239942	0.938	0.942	0	0	0	0.945	0.943	0	0	0.942	0.934	0.934	0.934	0.934	0.934	0.934	0.942	0.934	0.942	0.942	0.942	0.942	0.942	0	0	0	0	0	0.311
21	3.637687	0.010284	0.010785	openml__har__14970	0.978	0.99967	0.97765	0.061473	3.658756	6.869467	2.669442	2.666021	0.962	0.998168	0.962104	0.103502	12.204929	6	561	10,299	0.016	302.201547	370.311629	0.996	0.998	0	0	0	0.999	0.999	0	0	0.999	0.693	0.997	0.993	0.995	0.996	0.693	0.668	0.996	0.998	0.994	0.996	0.997	0.998	0	0	0	0	0	0
22	0.506032	0.000921	0.000765	openml__heart-c__48	0.839	0.901681	0.83871	0.384873	0.507718	0.007016	0.747218	0.573552	0.839	0.945378	0.83871	0.3051	1.327786	2	13	303	0	20.763378	39.833576	0.968	0.958	0	0	0	0.912	0.94	0.934	0.945	0.922	0.965	0.965	0.965	0.965	0.965	0.965	0.965	0.965	0.965	0.965	0.965	0.965	0.965	0.951	0	0	0	0	0.965
23	1.646659	0.008068	0.010839	openml__higgs__146606	0.714	0.789788	0.714227	0.550703	1.665566	0.029006	15.545551	15.544792	0.665	0.718481	0.664559	0.616014	31.119349	2	28	98,050	0.049	80.328152	931.453608	0.683	0.751	0	0	0	0.781	0.774	0	0	0.771	0.679	0.679	0.679	0.679	0.679	0.679	0.716	0.679	0.716	0.716	0.716	0.716	0.716	0	0	0	0	0.745	0
24	1.4285	0.001914	0.001344	openml__kc1__3917	0.848	0.782772	0.848341	0.370562	1.431757	0.003998	2.802546	2.804048	0.848	0.83345	0.848341	0.343852	5.610592	2	21	2,109	0	48.689536	168.44471	0.777	0.765	0	0	0	0.811	0.781	0	0	0.796	0.816	0.816	0.816	0.816	0.816	0.816	0.816	0.816	0	0	0	0	0	0	0	0	0	0	0.832
25	11.804756	0.565834	0.627261	openml__poker-hand__9890	0.946	0.904151	0.940036	0.294164	12.99785	0.204482	99.702475	100.086905	0.538	0.526818	0.460421	0.978269	199.993863	10	10	1,025,009	0.408	5,331.215826	2,422.621082	0.398	0.621	0	0	0	0.658	0.662	0	0	0.665	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
26	22.897829	0.044062	0.041597	openml__riccardo__168338	0.995	0.999323	0.9945	0.067689	22.983487	41.653156	2.850444	2.859816	0.955	0.994027	0.9545	0.236308	47.363416	2	4,296	20,000	0.04	692.07756	1,395.120139	0.921	0.99	0	0	0	0.995	0.996	0	0	0.996	0.491	0.899	0.953	0.899	0.913	0.491	0.494	0.888	0.98	0.982	0.979	0.984	0.973	0	0	0	0	0	0
27	21.521006	0.064051	0.064182	openml__robert__168332	0.455	0.863807	0.452383	1.550218	21.649238	82.247738	2.572722	2.570836	0.246	0.732318	0.21993	2.035677	87.391296	10	7,200	10,000	0.209	598.664213	963.968666	0.78	0.803	0	0	0	0.825	0.806	0	0	0.806	0.562	0.79	0.726	0.705	0.79	0.562	0.562	0.759	0.816	0.744	0.712	0.815	0.782	0	0	0	0	0	0
28	34.986799	0.100713	0.091736	openml__volkert__168331	0.67	0.923648	0.659887	0.956851	35.179248	3.614879	5.871648	5.867252	0.569	0.861787	0.511192	1.237312	15.353779	10	180	58,310	0.101	229.927867	458.743352	0.531	0.904	0	0	0	0.927	0.921	0	0	0.919	0.46	0.835	0	0.849	0.838	0.46	0.447	0.852	0.864	0	0.87	0.866	0.879	0	0	0	0	0	0
0	5.470622	0.308169	0.269775	openml__Agrawal1__146093	0.95	0.992252	0.95028	0.101591	6.048565	0.025716	216.104408	216.100852	0.946	0.991382	0.94613	0.118413	432.230976	2	9	1,000,000	0.004	142.218803	5,158.457872	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
1	6.712468	0.333047	0.279522	openml__BNG(labor)__2137	0.971	0.995435	0.9709	0.078912	7.325036	0.040458	229.750833	230.023553	0.938	0.982161	0.93836	0.15832	459.814844	2	16	1,000,000	0.033	226.240902	5,517.778131	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
2	1.588888	0.0934	0.089485	openml__BNG(vote)__212	0.975	0.996548	0.975359	0.069077	1.771773	0.01816	32.898037	32.942842	0.968	0.994736	0.967958	0.08944	65.859038	2	16	131,072	0.007	42.335094	1,975.77114	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3	7.746181	0.024661	0.039858	openml__Bioresponse__9910	0.803	0.878566	0.802667	0.441356	7.8107	15.971731	2.510121	2.535231	0.819	0.885325	0.818667	0.426595	21.017083	2	1,776	3,751	-0.016	112.880729	637.68252	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
4	3.756384	0.20875	0.17901	openml__Click_prediction_small__7294	0.842	0.74119	0.842075	0.393111	4.144144	0.004192	205.156692	205.236468	0.834	0.660425	0.83419	0.438609	410.397352	2	11	1,997,410	0.008	84.526018	10,420.932465	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
5	7.002172	0.165818	0.166777	openml__airlines__189354	0.664	0.715663	0.663842	0.610232	7.334767	0.107882	46.354915	46.322323	0.602	0.627777	0.601505	0.662777	92.785119	2	7	539,383	0.062	154.948461	2,601.905863	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
6	32.919919	1.06518	1.028028	openml__albert__189356	0.702	0.772683	0.701651	0.570534	35.013126	0.290836	61.830542	61.859957	0.637	0.686705	0.637146	0.642012	123.981335	2	78	425,240	0.065	1,077.832536	2,363.267322	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
7	2.096973	0.004232	0.003759	openml__balance-scale__11	0.871	0.92697	0.876643	0.315156	2.104964	0.003427	0.483712	0.480451	0.984	0.998659	0.984106	0.037652	0.967589	3	4	625	-0.113	52.771632	28.858381	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
8	0.524348	0.002498	0.001858	openml__blood-transfusion-service-center__10101	0.76	0.683723	0.76	0.529754	0.528704	0.0027	0.411178	0.408556	0.8	0.742203	0.8	0.48331	0.822434	2	4	748	-0.04	24.005331	24.855342	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
9	0.683357	0.002084	0.003628	openml__breast-cancer__145799	0.724	0.572222	0.724138	0.623272	0.689069	0.003648	0.491073	0.489	0.724	0.683333	0.724138	0.607276	0.983721	2	9	286	0	36.982686	28.809068	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
10	7.534318	0.122489	0.088212	openml__christine__168908	0.747	0.80579	0.747232	0.53844	7.745018	17.262028	2.645428	2.649614	0.718	0.803461	0.717712	0.543561	22.557071	2	1,636	5,418	0.029	331.16304	665.780806	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
11	0.710183	0.003037	0.001993	openml__climate-model-simulation-crashes__146819	0.931	0.919959	0.930864	0.181723	0.715213	0.003153	0.962894	0.960936	0.963	0.995918	0.962963	0.095939	1.926983	2	18	540	-0.032	24.901495	58.653851	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
12	1.767558	0.00417	0.003884	openml__cmc__23	0.541	0.724065	0.539462	0.905065	1.775612	0.007683	1.481638	1.517026	0.612	0.801547	0.610603	0.8145	3.006348	3	9	1,473	-0.071	78.094024	90.992666	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
13	3.11665	0.007943	0.008792	openml__colic__27	0.88	0.913657	0.88003	0.327921	3.133385	0.006265	1.039177	0.295073	0.878	0.925725	0.878378	0.339482	1.340514	2	22	368	0.002	218.034728	39.318812	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
14	16.109806	0.166454	0.075367	openml__connect-4__146195	0.821	0.871428	0.793513	0.473594	16.351627	0.078216	11.57487	11.56509	0.674	0.632796	0.561727	0.798451	23.218176	3	42	67,557	0.147	203.916567	696.121908	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
15	2.200719	0.00686	0.006669	openml__cylinder-bands__14954	0.787	0.875877	0.787037	0.443283	2.214247	0.00864	1.239279	0.138438	0.796	0.87798	0.796296	0.426672	1.386357	2	37	540	-0.009	93.963842	40.877432	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
16	12.705649	0.025386	0.024051	openml__dilbert__168909	0.962	0.998074	0.962014	0.189144	12.755086	20.246264	2.638321	2.643936	0.926	0.99487	0.926029	0.207929	25.52852	5	2,000	10,000	0.036	808.730207	761.177293	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
17	1.061346	0.002149	0.001866	openml__dresses-sales__125920	0.54	0.533662	0.54	0.737212	1.065361	0.004738	0.677673	0.674113	0.6	0.518883	0.6	0.680896	1.356524	2	12	500	-0.06	40.149266	40.880028	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
18	1.213773	0.004095	0.001866	openml__ecoli__145977	0.939	0.986417	0.940892	0.473745	1.219734	0.003157	0.520286	0.517488	0.727	0.933968	0.722274	0.808689	1.040931	8	7	336	0.212	73.058134	30.157485	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
19	0.785582	0.009653	0.009447	openml__eeg-eye-state__14951	0.892	0.959227	0.891856	0.290974	0.804682	0.009589	2.881329	2.88532	0.937	0.985335	0.93725	0.163974	5.776238	2	14	14,980	-0.045	27.734729	177.537891	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
20	2.590795	0.002214	0.003144	openml__elevators__3711	0.892	0.942216	0.891566	0.267698	2.596153	0.011596	3.048264	3.024167	0.895	0.954868	0.895181	0.235062	6.084026	2	18	16,599	-0.003	175.790243	186.239942	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
21	3.637687	0.010284	0.010785	openml__har__14970	0.978	0.99967	0.97765	0.061473	3.658756	6.869467	2.669442	2.666021	0.962	0.998168	0.962104	0.103502	12.204929	6	561	10,299	0.016	302.201547	370.311629	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
22	0.506032	0.000921	0.000765	openml__heart-c__48	0.839	0.901681	0.83871	0.384873	0.507718	0.007016	0.747218	0.573552	0.839	0.945378	0.83871	0.3051	1.327786	2	13	303	0	20.763378	39.833576	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
23	1.646659	0.008068	0.010839	openml__higgs__146606	0.714	0.789788	0.714227	0.550703	1.665566	0.029006	15.545551	15.544792	0.665	0.718481	0.664559	0.616014	31.119349	2	28	98,050	0.049	80.328152	931.453608	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
24	1.4285	0.001914	0.001344	openml__kc1__3917	0.848	0.782772	0.848341	0.370562	1.431757	0.003998	2.802546	2.804048	0.848	0.83345	0.848341	0.343852	5.610592	2	21	2,109	0	48.689536	168.44471	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
25	11.804756	0.565834	0.627261	openml__poker-hand__9890	0.946	0.904151	0.940036	0.294164	12.99785	0.204482	99.702475	100.086905	0.538	0.526818	0.460421	0.978269	199.993863	10	10	1,025,009	0.408	5,331.215826	2,422.621082	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
26	22.897829	0.044062	0.041597	openml__riccardo__168338	0.995	0.999323	0.9945	0.067689	22.983487	41.653156	2.850444	2.859816	0.955	0.994027	0.9545	0.236308	47.363416	2	4,296	20,000	0.04	692.07756	1,395.120139	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
27	21.521006	0.064051	0.064182	openml__robert__168332	0.455	0.863807	0.452383	1.550218	21.649238	82.247738	2.572722	2.570836	0.246	0.732318	0.21993	2.035677	87.391296	10	7,200	10,000	0.209	598.664213	963.968666	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
28	34.986799	0.100713	0.091736	openml__volkert__168331	0.67	0.923648	0.659887	0.956851	35.179248	3.614879	5.871648	5.867252	0.569	0.861787	0.511192	1.237312	15.353779	10	180	58,310	0.101	229.927867	458.743352	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
0	5.470622	0.308169	0.269775	openml__Agrawal1__146093	0.95	0.992252	0.95028	0.101591	6.048565	0.025716	216.104408	216.100852	0.946	0.991382	0.94613	0.118413	432.230976	2	9	1,000,000	0.004	142.218803	5,158.457872	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
1	6.712468	0.333047	0.279522	openml__BNG(labor)__2137	0.971	0.995435	0.9709	0.078912	7.325036	0.040458	229.750833	230.023553	0.938	0.982161	0.93836	0.15832	459.814844	2	16	1,000,000	0.033	226.240902	5,517.778131	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
2	1.588888	0.0934	0.089485	openml__BNG(vote)__212	0.975	0.996548	0.975359	0.069077	1.771773	0.01816	32.898037	32.942842	0.968	0.994736	0.967958	0.08944	65.859038	2	16	131,072	0.007	42.335094	1,975.77114	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
3	7.746181	0.024661	0.039858	openml__Bioresponse__9910	0.803	0.878566	0.802667	0.441356	7.8107	15.971731	2.510121	2.535231	0.819	0.885325	0.818667	0.426595	21.017083	2	1,776	3,751	-0.016	112.880729	637.68252	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
4	3.756384	0.20875	0.17901	openml__Click_prediction_small__7294	0.842	0.74119	0.842075	0.393111	4.144144	0.004192	205.156692	205.236468	0.834	0.660425	0.83419	0.438609	410.397352	2	11	1,997,410	0.008	84.526018	10,420.932465	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
5	7.002172	0.165818	0.166777	openml__airlines__189354	0.664	0.715663	0.663842	0.610232	7.334767	0.107882	46.354915	46.322323	0.602	0.627777	0.601505	0.662777	92.785119	2	7	539,383	0.062	154.948461	2,601.905863	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
6	32.919919	1.06518	1.028028	openml__albert__189356	0.702	0.772683	0.701651	0.570534	35.013126	0.290836	61.830542	61.859957	0.637	0.686705	0.637146	0.642012	123.981335	2	78	425,240	0.065	1,077.832536	2,363.267322	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
7	2.096973	0.004232	0.003759	openml__balance-scale__11	0.871	0.92697	0.876643	0.315156	2.104964	0.003427	0.483712	0.480451	0.984	0.998659	0.984106	0.037652	0.967589	3	4	625	-0.113	52.771632	28.858381	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
8	0.524348	0.002498	0.001858	openml__blood-transfusion-service-center__10101	0.76	0.683723	0.76	0.529754	0.528704	0.0027	0.411178	0.408556	0.8	0.742203	0.8	0.48331	0.822434	2	4	748	-0.04	24.005331	24.855342	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
9	0.683357	0.002084	0.003628	openml__breast-cancer__145799	0.724	0.572222	0.724138	0.623272	0.689069	0.003648	0.491073	0.489	0.724	0.683333	0.724138	0.607276	0.983721	2	9	286	0	36.982686	28.809068	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
10	7.534318	0.122489	0.088212	openml__christine__168908	0.747	0.80579	0.747232	0.53844	7.745018	17.262028	2.645428	2.649614	0.718	0.803461	0.717712	0.543561	22.557071	2	1,636	5,418	0.029	331.16304	665.780806	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
11	0.710183	0.003037	0.001993	openml__climate-model-simulation-crashes__146819	0.931	0.919959	0.930864	0.181723	0.715213	0.003153	0.962894	0.960936	0.963	0.995918	0.962963	0.095939	1.926983	2	18	540	-0.032	24.901495	58.653851	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
12	1.767558	0.00417	0.003884	openml__cmc__23	0.541	0.724065	0.539462	0.905065	1.775612	0.007683	1.481638	1.517026	0.612	0.801547	0.610603	0.8145	3.006348	3	9	1,473	-0.071	78.094024	90.992666	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
13	3.11665	0.007943	0.008792	openml__colic__27	0.88	0.913657	0.88003	0.327921	3.133385	0.006265	1.039177	0.295073	0.878	0.925725	0.878378	0.339482	1.340514	2	22	368	0.002	218.034728	39.318812	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
14	16.109806	0.166454	0.075367	openml__connect-4__146195	0.821	0.871428	0.793513	0.473594	16.351627	0.078216	11.57487	11.56509	0.674	0.632796	0.561727	0.798451	23.218176	3	42	67,557	0.147	203.916567	696.121908	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
15	2.200719	0.00686	0.006669	openml__cylinder-bands__14954	0.787	0.875877	0.787037	0.443283	2.214247	0.00864	1.239279	0.138438	0.796	0.87798	0.796296	0.426672	1.386357	2	37	540	-0.009	93.963842	40.877432	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
16	12.705649	0.025386	0.024051	openml__dilbert__168909	0.962	0.998074	0.962014	0.189144	12.755086	20.246264	2.638321	2.643936	0.926	0.99487	0.926029	0.207929	25.52852	5	2,000	10,000	0.036	808.730207	761.177293	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
17	1.061346	0.002149	0.001866	openml__dresses-sales__125920	0.54	0.533662	0.54	0.737212	1.065361	0.004738	0.677673	0.674113	0.6	0.518883	0.6	0.680896	1.356524	2	12	500	-0.06	40.149266	40.880028	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
18	1.213773	0.004095	0.001866	openml__ecoli__145977	0.939	0.986417	0.940892	0.473745	1.219734	0.003157	0.520286	0.517488	0.727	0.933968	0.722274	0.808689	1.040931	8	7	336	0.212	73.058134	30.157485	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
19	0.785582	0.009653	0.009447	openml__eeg-eye-state__14951	0.892	0.959227	0.891856	0.290974	0.804682	0.009589	2.881329	2.88532	0.937	0.985335	0.93725	0.163974	5.776238	2	14	14,980	-0.045	27.734729	177.537891	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
20	2.590795	0.002214	0.003144	openml__elevators__3711	0.892	0.942216	0.891566	0.267698	2.596153	0.011596	3.048264	3.024167	0.895	0.954868	0.895181	0.235062	6.084026	2	18	16,599	-0.003	175.790243	186.239942	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
21	3.637687	0.010284	0.010785	openml__har__14970	0.978	0.99967	0.97765	0.061473	3.658756	6.869467	2.669442	2.666021	0.962	0.998168	0.962104	0.103502	12.204929	6	561	10,299	0.016	302.201547	370.311629	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
22	0.506032	0.000921	0.000765	openml__heart-c__48	0.839	0.901681	0.83871	0.384873	0.507718	0.007016	0.747218	0.573552	0.839	0.945378	0.83871	0.3051	1.327786	2	13	303	0	20.763378	39.833576	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
23	1.646659	0.008068	0.010839	openml__higgs__146606	0.714	0.789788	0.714227	0.550703	1.665566	0.029006	15.545551	15.544792	0.665	0.718481	0.664559	0.616014	31.119349	2	28	98,050	0.049	80.328152	931.453608	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
24	1.4285	0.001914	0.001344	openml__kc1__3917	0.848	0.782772	0.848341	0.370562	1.431757	0.003998	2.802546	2.804048	0.848	0.83345	0.848341	0.343852	5.610592	2	21	2,109	0	48.689536	168.44471	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
25	11.804756	0.565834	0.627261	openml__poker-hand__9890	0.946	0.904151	0.940036	0.294164	12.99785	0.204482	99.702475	100.086905	0.538	0.526818	0.460421	0.978269	199.993863	10	10	1,025,009	0.408	5,331.215826	2,422.621082	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
26	22.897829	0.044062	0.041597	openml__riccardo__168338	0.995	0.999323	0.9945	0.067689	22.983487	41.653156	2.850444	2.859816	0.955	0.994027	0.9545	0.236308	47.363416	2	4,296	20,000	0.04	692.07756	1,395.120139	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
27	21.521006	0.064051	0.064182	openml__robert__168332	0.455	0.863807	0.452383	1.550218	21.649238	82.247738	2.572722	2.570836	0.246	0.732318	0.21993	2.035677	87.391296	10	7,200	10,000	0.209	598.664213	963.968666	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
28	34.986799	0.100713	0.091736	openml__volkert__168331	0.67	0.923648	0.659887	0.956851	35.179248	3.614879	5.871648	5.867252	0.569	0.861787	0.511192	1.237312	15.353779	10	180	58,310	0.101	229.927867	458.743352	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
0	5.470622	0.308169	0.269775	openml__Agrawal1__146093	0.95	0.992252	0.95028	0.101591	6.048565	0.025716	216.104408	216.100852	0.946	0.991382	0.94613	0.118413	432.230976	2	9	1,000,000	0.004	142.218803	5,158.457872	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
1	6.712468	0.333047	0.279522	openml__BNG(labor)__2137	0.971	0.995435	0.9709	0.078912	7.325036	0.040458	229.750833	230.023553	0.938	0.982161	0.93836	0.15832	459.814844	2	16	1,000,000	0.033	226.240902	5,517.778131	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
2	1.588888	0.0934	0.089485	openml__BNG(vote)__212	0.975	0.996548	0.975359	0.069077	1.771773	0.01816	32.898037	32.942842	0.968	0.994736	0.967958	0.08944	65.859038	2	16	131,072	0.007	42.335094	1,975.77114	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null

End of preview.

YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

This repository contains the extended raw results for the TuneTables paper.

These results, and the associated metadata, are in TabZilla format; see https://github.com/naszilla/tabzilla for more explanation.

Directory Structure

tabzilla_metadata: contains the performance results for most baseline methods reported in our paper (classification only)
excelformer: contains the performance results for the excelformer baseline method
regression: contains the baseline results for regression
datasets_used: descriptions of the datasets used in the paper, including OpenML IDs
TuneTables-Hard Results: 03-2024-tt-hard-main-plotting-data
TabZilla Results: 05-2024-tabzilla-main-plotting-data

If you find this repository useful, please consider citing our paper.

@misc{feuer2024tunetablescontextoptimizationscalable,
      title={TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks}, 
      author={Benjamin Feuer and Robin Tibor Schirrmeister and Valeriia Cherepanova and Chinmay Hegde and Frank Hutter and Micah Goldblum and Niv Cohen and Colin White},
      year={2024},
      eprint={2402.11137},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2402.11137}, 
}

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