Binary Classification Benchmarks

Dataset Overview

Quick Check

Adult

Source: OpenML · Original

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	0.88360 ± 0.00029	0.87303 ± 0.00086	0.87561 ± 0.00119	0.71620 ± 0.00276	0.78855 ± 0.00359	0.65602 ± 0.00337	0.93126 ± 0.00019	0.27150 ± 0.00043	0.64183 ± 0.00348	28.98606 ± 0.24880	—	—
LightGBM	0.88233 ± 0.00000	0.87033 ± 0.00000	0.87485 ± 0.00000	0.71548 ± 0.00000	0.78435 ± 0.00000	0.65773 ± 0.00000	0.93068 ± 0.00000	0.27285 ± 0.00000	0.64010 ± 0.00000	0.74575 ± 0.02078	—	—
XGBoost	0.89759 ± 0.00057	0.87377 ± 0.00088	0.87474 ± 0.00086	0.71367 ± 0.00186	0.78753 ± 0.00316	0.65248 ± 0.00247	0.93168 ± 0.00010	0.26967 ± 0.00020	0.63901 ± 0.00244	4.73544 ± 0.21290	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.86521 ± 0.00235	0.85607 ± 0.00092	0.85953 ± 0.00222	0.68127 ± 0.00440	0.74533 ± 0.00996	0.62750 ± 0.00866	0.91176 ± 0.00086	0.31164 ± 0.00128	0.59571 ± 0.00570	3,521 ± 0.00000	7,008 ± 0.00000	54.85229 ± 0.74675

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.83128 ± 0.00089	0.83729 ± 0.00108	0.83385 ± 0.00100	0.59862 ± 0.00506	0.70936 ± 0.00836	0.51797 ± 0.01116	0.88431 ± 0.00037	0.35248 ± 0.00050	0.50691 ± 0.00317	7,444,313 ± 0.00000	107,623,264,136 ± 230,007,694,241.9996	955.04623 ± 298.05726
IGTD	0.86015 ± 0.00557	0.85678 ± 0.00265	0.86158 ± 0.00332	0.68040 ± 0.00919	0.76002 ± 0.00810	0.61597 ± 0.01266	0.91413 ± 0.00414	0.30298 ± 0.00639	0.59867 ± 0.01054	5,392,857 ± 0.00000	287,033,393,073.59998 ± 628,903,515,621.57666	940.20070 ± 313.79190
REFINED	0.86328 ± 0.00182	0.85771 ± 0.00083	0.86191 ± 0.00254	0.68188 ± 0.00769	0.75960 ± 0.00736	0.61871 ± 0.01240	0.91688 ± 0.00101	0.29817 ± 0.00201	0.60003 ± 0.00826	21,741,465 ± 0.00000	4,628,581,563,982.59961 ± 9,329,933,670,002.79297	1,652.54363 ± 41.19085
DistanceMatrix	0.86464 ± 0.00151	0.85807 ± 0.00062	0.86185 ± 0.00161	0.68275 ± 0.00507	0.75787 ± 0.01098	0.62145 ± 0.01368	0.91661 ± 0.00097	0.29868 ± 0.00162	0.60047 ± 0.00451	3,296,737 ± 0.00000	1,834,562,810.4 ± 446,684,280.80716	4,508.00144 ± 375.06081
BarGraph	0.86968 ± 0.00270	0.86317 ± 0.00054	0.86450 ± 0.00094	0.68466 ± 0.00868	0.77271 ± 0.01430	0.61529 ± 0.02282	0.91980 ± 0.00131	0.29290 ± 0.00290	0.60640 ± 0.00525	24,031,129 ± 0.00000	6,258,016,842,372.7998 ± 13,901,105,812,226.26172	5,940.15903 ± 237.16990
Combination	0.86008 ± 0.00138	0.86022 ± 0.00110	0.86213 ± 0.00221	0.68678 ± 0.00726	0.75232 ± 0.00822	0.63194 ± 0.01422	0.91691 ± 0.00134	0.29941 ± 0.00295	0.60308 ± 0.00739	5,994,017 ± 0.00000	218,986,449,776 ± 480,295,508,944.83264	4,857.28221 ± 371.24368
SuperTML	0.87465 ± 0.00145	0.86934 ± 0.00112	0.87359 ± 0.00136	0.71088 ± 0.00414	0.78505 ± 0.00701	0.64963 ± 0.00917	0.92914 ± 0.00167	0.27369 ± 0.00296	0.63560 ± 0.00424	5,228,897 ± 0.00000	352,012,928,888.79999 ± 722,733,301,972.46082	6,181.48851 ± 339.90587
FeatureWrap	0.84682 ± 0.00293	0.84362 ± 0.00086	0.84272 ± 0.00232	0.62722 ± 0.00655	0.72443 ± 0.00803	0.55311 ± 0.00979	0.89529 ± 0.00143	0.33598 ± 0.00187	0.53761 ± 0.00717	2,422,585 ± 0.00000	867,308,785,689.59998 ± 1,919,113,357,454.8186	932.27980 ± 307.03148
BIE	0.87630 ± 0.00094	0.86852 ± 0.00071	0.87247 ± 0.00126	0.70830 ± 0.00466	0.78225 ± 0.00589	0.64723 ± 0.01011	0.92942 ± 0.00099	0.27481 ± 0.00214	0.63229 ± 0.00440	1,593,601 ± 0.00000	12,345,122,048 ± 27,115,085,185.77042	4,610.25863 ± 336.54583

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.86337 ± 0.00180	0.85403 ± 0.00114	0.85986 ± 0.00125	0.67875 ± 0.00722	0.75175 ± 0.01042	0.61905 ± 0.01772	0.91180 ± 0.00059	0.30952 ± 0.00105	0.59493 ± 0.00523	7,222,777 ± 0.00000	586,200,022,170.40002 ± 1,270,841,484,703.28784	1,056.53155 ± 334.00480
IGTD	0.86384 ± 0.00271	0.85703 ± 0.00054	0.86147 ± 0.00127	0.67790 ± 0.01154	0.76465 ± 0.02129	0.61015 ± 0.03098	0.91554 ± 0.00043	0.30127 ± 0.00071	0.59779 ± 0.00634	5,409,593 ± 0.00000	670,065,928,841.59998 ± 1,495,405,248,414.19141	1,093.33736 ± 374.21048
REFINED	0.86341 ± 0.00075	0.85793 ± 0.00031	0.86314 ± 0.00187	0.68828 ± 0.00689	0.75621 ± 0.00614	0.63172 ± 0.01324	0.91792 ± 0.00071	0.29653 ± 0.00097	0.60557 ± 0.00669	21,777,145 ± 0.00000	17,787,701,979,933.19922 ± 39,481,469,747,923.86719	1,873.85844 ± 65.74827
DistanceMatrix	0.86239 ± 0.00218	0.85591 ± 0.00110	0.85986 ± 0.00122	0.66759 ± 0.00849	0.77260 ± 0.02083	0.58871 ± 0.02553	0.91395 ± 0.00092	0.30392 ± 0.00085	0.58993 ± 0.00325	3,311,393 ± 0.00000	2,919,231,294.4 ± 852,405,747.02616	4,826.9087 ± 45.69974
BarGraph	0.86692 ± 0.00427	0.86113 ± 0.00152	0.86458 ± 0.00210	0.68986 ± 0.00924	0.76315 ± 0.01128	0.62989 ± 0.02054	0.91823 ± 0.00109	0.29553 ± 0.00194	0.60903 ± 0.00788	24,050,201 ± 0.00000	4,883,756,924,620.7998 ± 10,798,147,023,876.53906	6,173.47881 ± 267.93039
Combination	0.86802 ± 0.00200	0.86099 ± 0.00129	0.86264 ± 0.00135	0.68374 ± 0.00906	0.76121 ± 0.00819	0.62099 ± 0.02010	0.91747 ± 0.00177	0.29621 ± 0.00304	0.60240 ± 0.00697	5,958,881 ± 0.00000	7,196,016,444.8 ± 1,635,801,603.87701	4,978.12597 ± 387.62317
SuperTML	0.88046 ± 0.00154	0.86997 ± 0.00148	0.87274 ± 0.00118	0.71040 ± 0.00632	0.77981 ± 0.00789	0.65260 ± 0.01550	0.93130 ± 0.00115	0.27024 ± 0.00273	0.63390 ± 0.00516	5,233,441 ± 0.00000	37,056,822,293.6 ± 7,158,262,348.27	6,177.39169 ± 378.95922
FeatureWrap	0.86285 ± 0.00215	0.85665 ± 0.00252	0.85801 ± 0.00179	0.67467 ± 0.00224	0.74679 ± 0.01039	0.61540 ± 0.00787	0.91125 ± 0.00065	0.31072 ± 0.00144	0.58948 ± 0.00368	2,426,745 ± 0.00000	1,228,120,721,164.80005 ± 1,951,164,300,184.09937	1,091.41359 ± 369.05713
BIE	0.87888 ± 0.00244	0.86926 ± 0.00051	0.87103 ± 0.00185	0.70685 ± 0.00897	0.77513 ± 0.01649	0.65043 ± 0.02455	0.92784 ± 0.00171	0.27826 ± 0.00204	0.62930 ± 0.00648	1,609,857 ± 0.00000	2,645,073,401.6 ± 555,765,262.00882	4,946.27398 ± 467.90596

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.83209 ± 0.00174	0.83456 ± 0.00120	0.83352 ± 0.00163	0.60663 ± 0.02047	0.69957 ± 0.02721	0.53862 ± 0.04913	0.88401 ± 0.00072	0.35365 ± 0.00100	0.51172 ± 0.01088	100,193 ± 0.00000	1,684,666,163.2 ± 2,754,555,136.56125	478.57181 ± 160.01648
IGTD	0.86447 ± 0.00324	0.85616 ± 0.00192	0.85959 ± 0.00142	0.67791 ± 0.00607	0.75138 ± 0.00935	0.61780 ± 0.01458	0.91392 ± 0.00058	0.30404 ± 0.00116	0.59396 ± 0.00505	38,833 ± 0.00000	6,470,703,049.6 ± 11,396,710,036.89308	457.55727 ± 168.56427
REFINED	0.86487 ± 0.00277	0.85779 ± 0.00110	0.86229 ± 0.00088	0.68948 ± 0.00649	0.74896 ± 0.01374	0.63936 ± 0.02127	0.91550 ± 0.00092	0.30210 ± 0.00237	0.60510 ± 0.00386	12,174,849 ± 0.00000	1,720,528,502,553.6001 ± 1,569,858,666,121.05493	541.75531 ± 189.70280
DistanceMatrix	0.86197 ± 0.00147	0.85725 ± 0.00125	0.85940 ± 0.00208	0.68444 ± 0.00875	0.73906 ± 0.00692	0.63765 ± 0.01859	0.91542 ± 0.00127	0.30104 ± 0.00174	0.59744 ± 0.00848	2,790,801 ± 0.00000	8,200,082,621,056 ± 18,146,899,842,796.75781	6,518.84197 ± 261.08108
BarGraph	0.86547 ± 0.00157	0.86085 ± 0.00088	0.86090 ± 0.00154	0.67939 ± 0.00532	0.75731 ± 0.00508	0.61609 ± 0.00967	0.91640 ± 0.00079	0.29930 ± 0.00143	0.59694 ± 0.00528	7,644,897 ± 0.00000	9,004,026,309,913.59961 ± 19,597,071,669,553.15234	7,423.92973 ± 251.21881
Combination	0.87075 ± 0.00148	0.86380 ± 0.00049	0.86461 ± 0.00160	0.68443 ± 0.00971	0.77421 ± 0.01875	0.61426 ± 0.02639	0.92025 ± 0.00057	0.29313 ± 0.00074	0.60664 ± 0.00612	1,576,241 ± 0.00000	62,225,010,278.4 ± 16,984,745,198.02466	6,244.33529 ± 242.47695
SuperTML	0.87654 ± 0.00305	0.86921 ± 0.00074	0.87092 ± 0.00105	0.70455 ± 0.01283	0.77915 ± 0.01987	0.64438 ± 0.03320	0.92873 ± 0.00096	0.27546 ± 0.00236	0.62814 ± 0.00758	1,432,329 ± 0.00000	59,225,658,572.8 ± 23,105,015,668.63228	6,506.72809 ± 361.64444
FeatureWrap	0.84752 ± 0.00160	0.84592 ± 0.00131	0.84673 ± 0.00133	0.63646 ± 0.01153	0.73590 ± 0.01070	0.56132 ± 0.02392	0.90074 ± 0.00095	0.32895 ± 0.00138	0.54985 ± 0.00784	899,769 ± 0.00000	370,919,801,030.40002 ± 789,042,880,026.20093	747.08329 ± 284.61185
BIE	0.88156 ± 0.00547	0.86820 ± 0.00244	0.86925 ± 0.00191	0.70275 ± 0.01175	0.77077 ± 0.01686	0.64689 ± 0.03157	0.92401 ± 0.00215	0.28519 ± 0.00246	0.62419 ± 0.00940	415,385 ± 0.00000	389,462,076,129.59998 ± 852,031,168,299.22314	4,916.45485 ± 321.72315

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.86172 ± 0.00238	0.85296 ± 0.00084	0.85983 ± 0.00100	0.67662 ± 0.00294	0.75539 ± 0.01163	0.61301 ± 0.01254	0.91182 ± 0.00050	0.31034 ± 0.00163	0.59374 ± 0.00043	99,009 ± 0.00000	19,466,003,283.2 ± 27,266,599,498.37244	544.87111 ± 184.90659
IGTD	0.86247 ± 0.00033	0.85635 ± 0.00161	0.85888 ± 0.00228	0.67780 ± 0.00797	0.74737 ± 0.01681	0.62076 ± 0.02172	0.91260 ± 0.00053	0.30736 ± 0.00047	0.59291 ± 0.00661	42,425 ± 0.00000	4,091,177,942.4 ± 5,544,591,060.72648	553.18020 ± 212.15965
REFINED	0.86241 ± 0.00441	0.85643 ± 0.00091	0.86090 ± 0.00117	0.67987 ± 0.01179	0.75724 ± 0.02093	0.61837 ± 0.03476	0.91496 ± 0.00080	0.30327 ± 0.00155	0.59784 ± 0.00745	11,238,497 ± 0.00000	1,063,696,149,612.80005 ± 1,564,896,883,320.57617	656.11336 ± 243.87783
DistanceMatrix	0.86204 ± 0.00249	0.85673 ± 0.00169	0.86030 ± 0.00095	0.67165 ± 0.00641	0.76767 ± 0.01450	0.59749 ± 0.01848	0.91556 ± 0.00099	0.30197 ± 0.00159	0.59231 ± 0.00322	2,909,233 ± 0.00000	2,247,538,406,220.7998 ± 4,784,171,933,553.71582	6,372.80156 ± 468.18856
BarGraph	0.86637 ± 0.00446	0.85992 ± 0.00090	0.86174 ± 0.00166	0.67459 ± 0.01158	0.77283 ± 0.02208	0.59977 ± 0.02964	0.91764 ± 0.00157	0.29773 ± 0.00308	0.59679 ± 0.00626	7,750,529 ± 0.00000	6,288,807,287,379.2002 ± 13,433,949,662,267.45508	7,405.33008 ± 376.03781
Combination	0.87096 ± 0.00140	0.86353 ± 0.00061	0.86303 ± 0.00162	0.68312 ± 0.00661	0.76562 ± 0.01866	0.61746 ± 0.02224	0.91945 ± 0.00127	0.29420 ± 0.00223	0.60300 ± 0.00371	1,587,825 ± 0.00000	67,120,074,515.2 ± 13,956,301,557.83827	6,240.85022 ± 242.35425
SuperTML	0.87948 ± 0.00260	0.86871 ± 0.00124	0.87225 ± 0.00133	0.70655 ± 0.00922	0.78543 ± 0.02582	0.64358 ± 0.03119	0.93014 ± 0.00039	0.27299 ± 0.00156	0.63169 ± 0.00416	1,400,665 ± 0.00000	3,967,548,385,024 ± 8,735,996,902,048.83398	6,664.94851 ± 385.20819
FeatureWrap	0.86406 ± 0.00207	0.85856 ± 0.00044	0.85983 ± 0.00149	0.67871 ± 0.01130	0.75208 ± 0.01847	0.61962 ± 0.03130	0.91507 ± 0.00086	0.30407 ± 0.00219	0.59524 ± 0.00729	883,257 ± 0.00000	446,669,120,432 ± 661,605,539,735.07715	820.88079 ± 319.55702
BIE	0.87849 ± 0.00181	0.86836 ± 0.00139	0.87023 ± 0.00140	0.69858 ± 0.01165	0.78679 ± 0.02008	0.62943 ± 0.03211	0.92626 ± 0.00086	0.28148 ± 0.00370	0.62403 ± 0.00750	471,417 ± 0.00000	389,425,311,723.20001 ± 852,804,903,407.22351	4,964.46268 ± 304.83804

---

Bioresponse

Source: OpenML · Original

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	1.00000 ± 0.00000	0.79396 ± 0.00562	0.81456 ± 0.00463	0.82622 ± 0.00461	0.83912 ± 0.00567	0.81377 ± 0.00783	0.87989 ± 0.00397	0.50656 ± 0.01459	0.62793 ± 0.00928	25.20507 ± 0.05985	—	—
LightGBM	0.99992 ± 0.00017	0.80568 ± 0.00570	0.80888 ± 0.00728	0.82135 ± 0.00736	0.83205 ± 0.01089	0.81115 ± 0.01459	0.87810 ± 0.00307	0.47766 ± 0.00933	0.61636 ± 0.01457	1.24869 ± 0.04968	—	—
XGBoost	1.00000 ± 0.00000	0.79858 ± 0.00323	0.81847 ± 0.00477	0.83107 ± 0.00477	0.83800 ± 0.00371	0.82426 ± 0.00680	0.88961 ± 0.00122	0.43844 ± 0.00405	0.63506 ± 0.00942	11.42545 ± 0.10045	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.83040 ± 0.02068	0.75950 ± 0.01546	0.78615 ± 0.01182	0.79930 ± 0.01397	0.81263 ± 0.01049	0.78688 ± 0.02714	0.84194 ± 0.00613	0.49659 ± 0.00617	0.57126 ± 0.02221	1,041,409 ± 0.00000	2,082,048 ± 0.00000	13.43436 ± 0.30575

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.72556 ± 0.00357	0.69663 ± 0.00726	0.69130 ± 0.00918	0.74404 ± 0.01060	0.67546 ± 0.00969	0.82885 ± 0.02909	0.72977 ± 0.01074	0.59970 ± 0.00773	0.37811 ± 0.01951	1,716,385 ± 0.00000	85,861,632 ± 0.00000	39.12782 ± 0.22880
IGTD	0.85097 ± 0.03909	0.76199 ± 0.01589	0.74458 ± 0.02887	0.78704 ± 0.01479	0.72113 ± 0.03852	0.86885 ± 0.02820	0.82965 ± 0.01231	0.55979 ± 0.03481	0.49042 ± 0.05255	4,550,263 ± 0.00000	155,284,479 ± 0.00000	53.50183 ± 0.76464
REFINED	0.81509 ± 0.02089	0.75666 ± 0.00794	0.76945 ± 0.01214	0.78885 ± 0.00776	0.78407 ± 0.02504	0.79475 ± 0.02279	0.83747 ± 0.00358	0.51418 ± 0.00312	0.53588 ± 0.02559	4,426,201 ± 0.00000	43,919,946 ± 0.00000	67.72601 ± 1.34755
FeatureWrap	0.80160 ± 0.01612	0.73428 ± 0.01234	0.72220 ± 0.01321	0.73929 ± 0.00978	0.75338 ± 0.02814	0.72721 ± 0.02862	0.78393 ± 0.01199	0.57526 ± 0.01544	0.44340 ± 0.02995	9,800,161 ± 0.00000	333,217,900 ± 0.00000	63.76220 ± 1.49083

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.82156 ± 0.01455	0.76696 ± 0.00726	0.77336 ± 0.00463	0.78515 ± 0.00592	0.80698 ± 0.00511	0.76459 ± 0.01257	0.83574 ± 0.00424	0.50571 ± 0.00383	0.54669 ± 0.00848	2,657,889 ± 0.00000	809,617,408 ± 160,014,690.16261	62.18761 ± 6.29526
IGTD	0.82278 ± 0.02320	0.74884 ± 0.01099	0.76909 ± 0.01554	0.79588 ± 0.00733	0.76581 ± 0.03178	0.83016 ± 0.02565	0.83846 ± 0.00896	0.50748 ± 0.01806	0.53493 ± 0.03077	5,871,831 ± 0.00000	3,481,690,302 ± 305,835,318.37888	73.41846 ± 1.17743
REFINED	0.83413 ± 0.02558	0.77016 ± 0.00945	0.77620 ± 0.01066	0.78976 ± 0.01479	0.80488 ± 0.02779	0.77770 ± 0.04575	0.84131 ± 0.00261	0.51031 ± 0.01926	0.55282 ± 0.02032	5,496,729 ± 0.00000	637,718,480 ± 126,040,181.42378	85.62639 ± 2.60948
FeatureWrap	0.83688 ± 0.00904	0.76838 ± 0.00937	0.78224 ± 0.01070	0.79224 ± 0.01093	0.81974 ± 0.00857	0.76656 ± 0.01340	0.83531 ± 0.00602	0.50985 ± 0.01106	0.56542 ± 0.02094	10,885,697 ± 0.00000	7,426,666,584 ± 652,366,161.89173	83.09175 ± 5.88656

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.76709 ± 0.01971	0.69663 ± 0.01091	0.70444 ± 0.01650	0.75249 ± 0.00673	0.69077 ± 0.02870	0.82951 ± 0.04423	0.75853 ± 0.01253	0.58758 ± 0.00870	0.40618 ± 0.02840	30,369 ± 0.00000	6,680,388 ± 0.00000	34.07241 ± 0.46630
IGTD	0.81432 ± 0.10319	0.62948 ± 0.02790	0.63197 ± 0.08147	0.69982 ± 0.03777	0.65871 ± 0.10504	0.79934 ± 0.19212	0.75447 ± 0.03277	0.69259 ± 0.11148	0.24764 ± 0.19178	5,417,457 ± 0.00000	978,246,400 ± 0.00000	51.55007 ± 1.14411
REFINED	0.91657 ± 0.04280	0.76590 ± 0.01434	0.79183 ± 0.01769	0.80690 ± 0.01185	0.81448 ± 0.04146	0.80262 ± 0.04169	0.85621 ± 0.00549	0.48291 ± 0.01118	0.58378 ± 0.03607	2,715,313 ± 0.00000	1,506,688,896 ± 0.00000	56.44847 ± 0.36734
FeatureWrap	0.85836 ± 0.03730	0.73961 ± 0.01788	0.73996 ± 0.01592	0.75638 ± 0.03293	0.76865 ± 0.03896	0.75279 ± 0.08763	0.80743 ± 0.00882	0.54629 ± 0.01841	0.48259 ± 0.02264	5,155,537 ± 0.00000	2,894,869,760 ± 0.00000	77.51294 ± 0.31586

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.83718 ± 0.02015	0.75489 ± 0.00879	0.77869 ± 0.00749	0.79065 ± 0.01160	0.81068 ± 0.01265	0.77246 ± 0.03098	0.84023 ± 0.00584	0.50143 ± 0.00800	0.55757 ± 0.01286	1,088,657 ± 0.00000	184,719,528 ± 0.00000	52.17665 ± 2.32481
IGTD	0.88541 ± 0.05204	0.64192 ± 0.05350	0.70764 ± 0.02508	0.73493 ± 0.04397	0.73387 ± 0.08276	0.76656 ± 0.15670	0.80774 ± 0.02291	0.60415 ± 0.08857	0.42959 ± 0.05164	6,056,049 ± 0.00000	979,523,520 ± 0.00000	59.26457 ± 1.49678
REFINED	0.94271 ± 0.02542	0.76696 ± 0.01201	0.78686 ± 0.01036	0.80513 ± 0.00924	0.80144 ± 0.04783	0.81443 ± 0.05808	0.85253 ± 0.01044	0.50015 ± 0.02944	0.57440 ± 0.02428	3,799,729 ± 0.00000	1,508,856,848 ± 0.00000	63.84759 ± 1.33045
FeatureWrap	0.90126 ± 0.03365	0.74529 ± 0.00269	0.74423 ± 0.00879	0.75265 ± 0.02133	0.79149 ± 0.03435	0.72197 ± 0.06192	0.81223 ± 0.01007	0.54734 ± 0.02279	0.49388 ± 0.01438	5,999,569 ± 0.00000	2,896,557,568 ± 0.00000	81.22601 ± 0.07450

---

Credit-approval

Source: OpenML · Original

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	0.85300 ± 0.00000	0.83495 ± 0.00000	0.88462 ± 0.00000	0.88679 ± 0.00000	0.97917 ± 0.00000	0.81034 ± 0.00000	0.94858 ± 0.00155	0.34290 ± 0.00159	0.78567 ± 0.00000	4.35128 ± 0.10240	—	—
LightGBM	0.76232 ± 0.00708	0.77087 ± 0.01472	0.75577 ± 0.00527	0.81780 ± 0.00321	0.70027 ± 0.00470	0.98276 ± 0.00000	0.95064 ± 0.00133	0.61286 ± 0.00230	0.54471 ± 0.00958	0.09859 ± 0.00641	—	—
XGBoost	0.71801 ± 0.00307	0.74757 ± 0.00686	0.76154 ± 0.00430	0.82133 ± 0.00266	0.70546 ± 0.00393	0.98276 ± 0.00000	0.94910 ± 0.00081	0.61517 ± 0.00064	0.55518 ± 0.00777	0.48363 ± 0.00575	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.88323 ± 0.00185	0.84466 ± 0.00000	0.87692 ± 0.00430	0.88363 ± 0.00421	0.93474 ± 0.00934	0.83793 ± 0.00944	0.94468 ± 0.00151	0.29493 ± 0.00127	0.75903 ± 0.00888	27,137 ± 0.00000	53,760 ± 0.00000	6.46258 ± 0.03859

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.84679 ± 0.00971	0.91068 ± 0.01737	0.88654 ± 0.02193	0.90270 ± 0.01715	0.86791 ± 0.03361	0.94138 ± 0.01966	0.95165 ± 0.00314	0.28738 ± 0.01023	0.77210 ± 0.04305	1,600,609 ± 0.00000	83,777,096 ± 0.00000	24.18381 ± 0.14994
IGTD	0.88447 ± 0.02241	0.87767 ± 0.00532	0.84423 ± 0.01580	0.86430 ± 0.01354	0.84125 ± 0.02458	0.88965 ± 0.02885	0.93426 ± 0.00694	0.33153 ± 0.01703	0.68444 ± 0.03281	19,520,281 ± 0.00000	667,902,402 ± 0.00000	68.75373 ± 0.10999
REFINED	0.86460 ± 0.00236	0.86796 ± 0.01107	0.87500 ± 0.00680	0.88368 ± 0.00721	0.91834 ± 0.00851	0.85172 ± 0.01542	0.94468 ± 0.00179	0.28699 ± 0.00243	0.75166 ± 0.01246	5,268,487 ± 0.00000	696,741,440 ± 0.00000	29.86047 ± 0.45036
DistanceMatrix	0.86170 ± 0.02108	0.87379 ± 0.01373	0.86538 ± 0.02150	0.87773 ± 0.01787	0.89253 ± 0.04424	0.86552 ± 0.03316	0.94205 ± 0.00545	0.29659 ± 0.01014	0.73031 ± 0.04643	3,595,591 ± 0.00000	71,253,567 ± 0.00000	28.01472 ± 0.10554
BarGraph	0.89151 ± 0.00664	0.88544 ± 0.02105	0.86538 ± 0.01799	0.87968 ± 0.01632	0.87735 ± 0.02393	0.88276 ± 0.02833	0.93486 ± 0.00285	0.31011 ± 0.01131	0.72776 ± 0.03709	1,036,999 ± 0.00000	20,876,839 ± 0.00000	33.32826 ± 2.41426
Combination	0.88696 ± 0.01339	0.87573 ± 0.01064	0.86923 ± 0.01874	0.88301 ± 0.01423	0.88397 ± 0.03368	0.88276 ± 0.00771	0.93613 ± 0.00461	0.31838 ± 0.01974	0.73552 ± 0.03934	2,364,615 ± 0.00000	47,539,495 ± 0.00000	43.79120 ± 0.31413
SuperTML	0.85010 ± 0.02483	0.87184 ± 0.02513	0.86731 ± 0.01720	0.88065 ± 0.00823	0.89316 ± 0.06675	0.87586 ± 0.06264	0.95547 ± 0.00301	0.28118 ± 0.00674	0.73949 ± 0.03251	11,972,449 ± 0.00000	1,580,762,012 ± 0.00000	55.46818 ± 0.95789
FeatureWrap	0.84969 ± 0.01540	0.91262 ± 0.01535	0.89808 ± 0.03010	0.91357 ± 0.02319	0.87394 ± 0.04923	0.95862 ± 0.00944	0.95735 ± 0.00327	0.26809 ± 0.01913	0.79724 ± 0.05758	7,265,761 ± 0.00000	72,676,493 ± 0.00000	24.34938 ± 0.09730
BIE	0.88282 ± 0.02517	0.87767 ± 0.00868	0.87308 ± 0.01580	0.88563 ± 0.01540	0.89001 ± 0.02562	0.88276 ± 0.03738	0.94678 ± 0.01051	0.28848 ± 0.02308	0.74448 ± 0.03101	1,831,681 ± 0.00000	62,989,904 ± 0.00000	31.80109 ± 0.45090

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.86211 ± 0.03239	0.88350 ± 0.01535	0.87308 ± 0.02394	0.88714 ± 0.01516	0.88942 ± 0.05987	0.88965 ± 0.04496	0.94145 ± 0.01022	0.30223 ± 0.02367	0.74784 ± 0.04481	2,153,313 ± 0.00000	774,421,568 ± 153,058,501.50243	36.11794 ± 0.54655
IGTD	0.90228 ± 0.02078	0.86602 ± 0.01990	0.85577 ± 0.02255	0.86746 ± 0.02201	0.88956 ± 0.03197	0.84828 ± 0.04463	0.93516 ± 0.00514	0.32251 ± 0.02280	0.71212 ± 0.04505	19,548,953 ± 0.00000	6,611,922,832 ± 1,306,795,991.39943	88.77799 ± 1.40963
REFINED	0.86335 ± 0.00414	0.86019 ± 0.00868	0.87692 ± 0.00430	0.88408 ± 0.00269	0.93162 ± 0.01586	0.84138 ± 0.00771	0.95052 ± 0.00314	0.27924 ± 0.00912	0.75825 ± 0.01185	5,142,983 ± 0.00000	13,348,791,360 ± 1,172,571,797.4438	37.56043 ± 0.44217
DistanceMatrix	0.87371 ± 0.00146	0.85437 ± 0.01189	0.87308 ± 0.00430	0.87775 ± 0.00454	0.94842 ± 0.01573	0.81724 ± 0.01542	0.93883 ± 0.00282	0.30097 ± 0.01016	0.75654 ± 0.00987	3,738,311 ± 0.00000	779,787,256 ± 154,118,988.7083	33.55061 ± 1.04348
BarGraph	0.90559 ± 0.01386	0.88155 ± 0.01266	0.87308 ± 0.01053	0.88457 ± 0.00988	0.89774 ± 0.01961	0.87241 ± 0.02313	0.93313 ± 0.00213	0.31967 ± 0.01001	0.74466 ± 0.02161	1,084,551 ± 0.00000	803,412,036 ± 45,368,070.23845	47.16871 ± 1.24050
Combination	0.87246 ± 0.01673	0.86019 ± 0.02532	0.87308 ± 0.00430	0.88189 ± 0.00790	0.91746 ± 0.03598	0.85172 ± 0.04496	0.93493 ± 0.00697	0.31712 ± 0.01660	0.75004 ± 0.00846	2,436,903 ± 0.00000	527,485,752 ± 104,253,525.60042	58.56323 ± 6.61817
SuperTML	0.87205 ± 0.03957	0.86602 ± 0.02105	0.86346 ± 0.01850	0.87717 ± 0.01091	0.88901 ± 0.06413	0.87241 ± 0.06047	0.93801 ± 0.01346	0.30456 ± 0.03170	0.73106 ± 0.03564	12,460,001 ± 0.00000	30,112,373,304 ± 2,645,102,372.0525	74.32829 ± 5.53914
FeatureWrap	0.89317 ± 0.02145	0.87961 ± 0.02013	0.89038 ± 0.01458	0.90311 ± 0.01369	0.89089 ± 0.02661	0.91724 ± 0.03738	0.94288 ± 0.00435	0.28508 ± 0.01758	0.77931 ± 0.02865	7,325,569 ± 0.00000	3,665,691,148 ± 206,998,807.61424	35.66323 ± 0.36383
BIE	0.88654 ± 0.00556	0.86602 ± 0.01868	0.88462 ± 0.00962	0.89051 ± 0.00897	0.94643 ± 0.02228	0.84138 ± 0.01889	0.95232 ± 0.00606	0.27763 ± 0.01075	0.77586 ± 0.02049	1,859,585 ± 0.00000	1,387,447,200 ± 121,874,813.47842	40.93451 ± 0.83433

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.89068 ± 0.01271	0.86990 ± 0.02437	0.87885 ± 0.01609	0.88831 ± 0.01599	0.91333 ± 0.01960	0.86552 ± 0.03316	0.93920 ± 0.00886	0.32099 ± 0.02614	0.75827 ± 0.03103	108,793 ± 0.00000	16,313,808 ± 0.00000	22.76856 ± 0.21394
IGTD	0.90393 ± 0.00999	0.84272 ± 0.03527	0.85577 ± 0.01923	0.86486 ± 0.02211	0.90302 ± 0.01110	0.83103 ± 0.04463	0.93861 ± 0.00790	0.33714 ± 0.04084	0.71460 ± 0.03237	2,758,497 ± 0.00000	26,344,896 ± 0.00000	34.86336 ± 0.12589
REFINED	0.90104 ± 0.01422	0.85049 ± 0.01107	0.86731 ± 0.01971	0.87678 ± 0.02234	0.90918 ± 0.04680	0.85172 ± 0.06744	0.94318 ± 0.00314	0.33025 ± 0.00821	0.73999 ± 0.03647	2,112,225 ± 0.00000	20,382,208 ± 0.00000	19.94357 ± 0.08692
DistanceMatrix	0.87081 ± 0.01041	0.88544 ± 0.01266	0.85577 ± 0.02150	0.87059 ± 0.01765	0.87447 ± 0.04377	0.86897 ± 0.03575	0.94071 ± 0.00797	0.30352 ± 0.01824	0.71000 ± 0.04691	431,553 ± 0.00000	321,349,776 ± 0.00000	32.35910 ± 2.07113
BarGraph	0.87743 ± 0.00846	0.84854 ± 0.01764	0.87115 ± 0.01994	0.87950 ± 0.01609	0.92317 ± 0.04319	0.84138 ± 0.02557	0.94483 ± 0.00932	0.28156 ± 0.01421	0.74679 ± 0.04371	189,889 ± 0.00000	132,799,648 ± 0.00000	24.68416 ± 0.52450
Combination	0.85756 ± 0.02178	0.86990 ± 0.01893	0.87692 ± 0.02085	0.88591 ± 0.02024	0.91658 ± 0.02910	0.85862 ± 0.03932	0.93801 ± 0.00948	0.29441 ± 0.01778	0.75576 ± 0.04130	1,126,289 ± 0.00000	729,226,960 ± 0.00000	30.61405 ± 0.78141
SuperTML	0.86170 ± 0.03083	0.87379 ± 0.01942	0.85962 ± 0.03160	0.87766 ± 0.02171	0.86397 ± 0.06470	0.89655 ± 0.04043	0.93546 ± 0.01064	0.31527 ± 0.01634	0.71950 ± 0.06294	357,681 ± 0.00000	244,017,024 ± 0.00000	56.27273 ± 1.87451
FeatureWrap	0.86128 ± 0.02034	0.89709 ± 0.03039	0.87885 ± 0.02413	0.89497 ± 0.01722	0.87491 ± 0.06307	0.92069 ± 0.04152	0.93966 ± 0.01339	0.31791 ± 0.03718	0.75874 ± 0.05186	1,679,265 ± 0.00000	26,047,680 ± 0.00000	21.80783 ± 0.12512
BIE	0.99959 ± 0.00093	0.89709 ± 0.02437	0.82692 ± 0.02040	0.85457 ± 0.01417	0.80599 ± 0.02899	0.91034 ± 0.01889	0.90180 ± 0.01179	0.49047 ± 0.09350	0.65132 ± 0.03901	1,219,369 ± 0.00000	452,641,280 ± 0.00000	23.68857 ± 1.57080

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.86915 ± 0.01415	0.86602 ± 0.01440	0.86731 ± 0.02489	0.87889 ± 0.02100	0.89853 ± 0.04504	0.86207 ± 0.03448	0.93636 ± 0.00426	0.30994 ± 0.03336	0.73497 ± 0.05269	170,793 ± 0.00000	16,437,264 ± 0.00000	23.51064 ± 1.06906
IGTD	0.90435 ± 0.00895	0.85049 ± 0.01303	0.88462 ± 0.00962	0.89128 ± 0.00918	0.93942 ± 0.01918	0.84828 ± 0.01889	0.93546 ± 0.01047	0.31789 ± 0.02048	0.77397 ± 0.02008	2,735,457 ± 0.00000	26,298,688 ± 0.00000	36.58546 ± 0.15293
REFINED	0.89524 ± 0.00910	0.84660 ± 0.01266	0.87885 ± 0.00860	0.88479 ± 0.00862	0.94184 ± 0.01278	0.83448 ± 0.01542	0.94160 ± 0.00370	0.30438 ± 0.00790	0.76438 ± 0.01678	2,491,169 ± 0.00000	21,139,456 ± 0.00000	21.89030 ± 0.07673
DistanceMatrix	0.85632 ± 0.02390	0.85243 ± 0.02417	0.88077 ± 0.01874	0.89164 ± 0.01540	0.90919 ± 0.05523	0.87931 ± 0.05172	0.94430 ± 0.00532	0.31482 ± 0.02456	0.76424 ± 0.03944	488,481 ± 0.00000	321,463,144 ± 0.00000	35.87942 ± 2.03683
BarGraph	0.88157 ± 0.01220	0.84078 ± 0.01764	0.87308 ± 0.01426	0.87865 ± 0.01386	0.94121 ± 0.01852	0.82414 ± 0.01889	0.94348 ± 0.00464	0.29809 ± 0.00829	0.75430 ± 0.02864	219,425 ± 0.00000	132,858,336 ± 0.00000	35.27408 ± 3.46612
Combination	0.86584 ± 0.02241	0.84272 ± 0.01595	0.85769 ± 0.02753	0.87115 ± 0.02328	0.88503 ± 0.05529	0.86207 ± 0.05314	0.93973 ± 0.00262	0.32083 ± 0.02672	0.71694 ± 0.05566	1,317,009 ± 0.00000	15,321,762,960 ± 0.00000	49.18553 ± 2.91262
SuperTML	0.90145 ± 0.03785	0.86602 ± 0.02214	0.86731 ± 0.00805	0.88339 ± 0.00958	0.86725 ± 0.03440	0.90345 ± 0.05115	0.93666 ± 0.00719	0.33066 ± 0.02660	0.73433 ± 0.01701	441,649 ± 0.00000	244,184,512 ± 0.00000	56.67473 ± 4.74708
FeatureWrap	0.88654 ± 0.01184	0.86214 ± 0.02318	0.87115 ± 0.00860	0.87876 ± 0.00864	0.92557 ± 0.03114	0.83793 ± 0.03132	0.94408 ± 0.00311	0.29397 ± 0.01804	0.74744 ± 0.01996	1,775,201 ± 0.00000	26,239,200 ± 0.00000	23.54730 ± 0.12061
BIE	0.97971 ± 0.02833	0.87573 ± 0.00812	0.85385 ± 0.02085	0.87046 ± 0.01693	0.86260 ± 0.03206	0.87931 ± 0.02112	0.92144 ± 0.01176	0.35882 ± 0.03894	0.70393 ± 0.04291	1,475,625 ± 0.00000	453,152,896 ± 0.00000	26.65689 ± 1.21066

---

Dengue chikunguya bin

Source: Mendeley Data

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	0.84445 ± 0.00445	0.79685 ± 0.00350	0.77381 ± 0.00152	0.76203 ± 0.00216	0.80388 ± 0.00192	0.72433 ± 0.00431	0.84859 ± 0.00078	0.48129 ± 0.00036	0.55032 ± 0.00287	18.33763 ± 0.20578	—	—
LightGBM	0.82621 ± 0.00128	0.79476 ± 0.00146	0.76601 ± 0.00567	0.75396 ± 0.00664	0.79484 ± 0.00538	0.71711 ± 0.00887	0.84481 ± 0.00028	0.48559 ± 0.00063	0.53459 ± 0.01116	0.66354 ± 0.06674	—	—
XGBoost	0.84830 ± 0.00055	0.79080 ± 0.00096	0.77055 ± 0.00133	0.75896 ± 0.00132	0.79934 ± 0.00258	0.72247 ± 0.00241	0.85063 ± 0.00045	0.47857 ± 0.00069	0.54362 ± 0.00277	1.87975 ± 0.05041	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.79361 ± 0.00407	0.78626 ± 0.00193	0.75250 ± 0.00428	0.73198 ± 0.00265	0.79837 ± 0.01122	0.67590 ± 0.00556	0.82654 ± 0.00162	0.52665 ± 0.01087	0.51113 ± 0.01005	49,665 ± 0.00000	98,944 ± 0.00000	32.29673 ± 0.16948

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.78670 ± 0.00351	0.77787 ± 0.00525	0.75215 ± 0.00440	0.73795 ± 0.00382	0.78317 ± 0.01386	0.69802 ± 0.01425	0.82601 ± 0.00283	0.51071 ± 0.00549	0.50754 ± 0.00993	37,861,015 ± 0.00000	1,293,369,808 ± 0.00000	411.74121 ± 0.77761
IGTD	0.79044 ± 0.00466	0.78439 ± 0.00581	0.75367 ± 0.00289	0.73667 ± 0.00502	0.79174 ± 0.01661	0.68941 ± 0.02047	0.82431 ± 0.00290	0.51494 ± 0.00502	0.51201 ± 0.00780	4,190,743 ± 0.00000	78,083,135 ± 0.00000	121.39114 ± 0.81683
REFINED	0.79436 ± 0.00477	0.78626 ± 0.00184	0.75786 ± 0.00519	0.73684 ± 0.00985	0.80677 ± 0.00859	0.67846 ± 0.02067	0.82813 ± 0.00187	0.50985 ± 0.00669	0.52256 ± 0.00842	7,342,369 ± 0.00000	73,412,898 ± 0.00000	237.12990 ± 1.57417
DistanceMatrix	0.79483 ± 0.00728	0.78451 ± 0.00250	0.75926 ± 0.00249	0.74453 ± 0.00775	0.79344 ± 0.01756	0.70221 ± 0.02597	0.82720 ± 0.00331	0.51223 ± 0.00760	0.52250 ± 0.00606	13,839,809 ± 0.00000	135,836,308 ± 0.00000	280.96291 ± 5.19273
BarGraph	0.79546 ± 0.00396	0.78684 ± 0.00442	0.76216 ± 0.00383	0.74587 ± 0.00265	0.80087 ± 0.00926	0.69802 ± 0.00470	0.83119 ± 0.00293	0.51191 ± 0.00573	0.52876 ± 0.00867	3,948,545 ± 0.00000	37,579,600 ± 0.00000	102.67005 ± 0.95858
Combination	0.79391 ± 0.00334	0.78579 ± 0.00223	0.75867 ± 0.00346	0.74347 ± 0.00374	0.79352 ± 0.00676	0.69942 ± 0.00705	0.82984 ± 0.00273	0.50759 ± 0.00658	0.52106 ± 0.00722	26,974,465 ± 0.00000	80,410,966,806 ± 176,895,483,141.79083	1,163.32768 ± 262.76441
SuperTML	0.79558 ± 0.00533	0.78870 ± 0.00437	0.76519 ± 0.00515	0.75174 ± 0.00573	0.79756 ± 0.01036	0.71106 ± 0.01154	0.83537 ± 0.00185	0.50311 ± 0.00267	0.53364 ± 0.01077	3,580,129 ± 0.00000	57,361,400,250.6 ± 116,728,731,983.64003	1,542.52599 ± 158.45189
FeatureWrap	0.77636 ± 0.00341	0.76622 ± 0.00370	0.73842 ± 0.00550	0.71831 ± 0.00799	0.77822 ± 0.01116	0.66729 ± 0.01702	0.81591 ± 0.00189	0.52766 ± 0.00519	0.48193 ± 0.01103	9,475,609 ± 0.00000	329,832,766,077.59998 ± 721,087,062,859.04285	459.02159 ± 129.85201
BIE	0.80192 ± 0.00874	0.78381 ± 0.00577	0.76252 ± 0.00123	0.75060 ± 0.00198	0.79025 ± 0.00376	0.71479 ± 0.00570	0.82890 ± 0.00235	0.50835 ± 0.00301	0.52746 ± 0.00251	16,484,193 ± 0.00000	2,471,681,549,289.6001 ± 5,428,955,286,813.38867	1,305.07611 ± 303.07968

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.79638 ± 0.00401	0.78486 ± 0.00367	0.75506 ± 0.00161	0.73767 ± 0.00403	0.79426 ± 0.01143	0.68894 ± 0.01490	0.82593 ± 0.00241	0.52127 ± 0.00595	0.51486 ± 0.00463	38,117,399 ± 0.00000	429,126,782,022.40002 ± 934,424,394,897.82361	1,300.61963 ± 320.98518
IGTD	0.79910 ± 0.00335	0.78486 ± 0.00456	0.75623 ± 0.00312	0.73737 ± 0.00728	0.79927 ± 0.01021	0.68475 ± 0.01876	0.82426 ± 0.00154	0.52813 ± 0.00397	0.51801 ± 0.00523	4,122,519 ± 0.00000	33,085,208,539.6 ± 70,040,658,830.16013	610.68185 ± 172.91366
REFINED	0.79286 ± 0.00469	0.78497 ± 0.00199	0.75635 ± 0.00290	0.74048 ± 0.00257	0.79211 ± 0.00645	0.69523 ± 0.00503	0.82637 ± 0.00128	0.51844 ± 0.00432	0.51660 ± 0.00630	7,393,185 ± 0.00000	66,861,683,869.6 ± 142,595,257,258.11005	1,116.93216 ± 318.84188
DistanceMatrix	0.79391 ± 0.00264	0.78812 ± 0.00262	0.75669 ± 0.00214	0.73828 ± 0.00191	0.79889 ± 0.00951	0.68638 ± 0.00919	0.82638 ± 0.00133	0.52348 ± 0.00401	0.51865 ± 0.00561	14,030,369 ± 0.00000	735,078,836,909.59998 ± 1,623,592,589,055.5498	1,398.96918 ± 401.01450
BarGraph	0.80155 ± 0.00745	0.78893 ± 0.00360	0.76275 ± 0.00320	0.74863 ± 0.00609	0.79620 ± 0.01265	0.70687 ± 0.01843	0.83142 ± 0.00322	0.51920 ± 0.00647	0.52910 ± 0.00669	4,180,033 ± 0.00000	26,258,935,508.8 ± 54,578,732,372.61453	541.91954 ± 149.32681
Combination	0.79311 ± 0.00439	0.78660 ± 0.00356	0.75413 ± 0.00252	0.73493 ± 0.00231	0.79741 ± 0.01073	0.68172 ± 0.01033	0.82560 ± 0.00200	0.52567 ± 0.00832	0.51382 ± 0.00655	27,468,833 ± 0.00000	1,025,750,154,648.40002 ± 2,246,662,723,183.00537	1,776.51478 ± 517.27827
SuperTML	0.79923 ± 0.00855	0.78975 ± 0.00578	0.76356 ± 0.00603	0.74648 ± 0.00806	0.80467 ± 0.01058	0.69639 ± 0.01576	0.83214 ± 0.00350	0.50869 ± 0.00323	0.53211 ± 0.01183	3,795,105 ± 0.00000	1,011,732,695,367.59998 ± 2,196,801,926,319.54199	1,777.37847 ± 255.34242
FeatureWrap	0.79666 ± 0.00211	0.78742 ± 0.00229	0.75565 ± 0.00215	0.73703 ± 0.00621	0.79812 ± 0.01516	0.68522 ± 0.02017	0.82669 ± 0.00224	0.52503 ± 0.00428	0.51682 ± 0.00571	9,617,049 ± 0.00000	845,407,410,135.19995 ± 1,807,053,346,291.28271	808.30747 ± 277.02514
BIE	0.79705 ± 0.00581	0.78649 ± 0.00200	0.76042 ± 0.00275	0.74338 ± 0.00327	0.80036 ± 0.00689	0.69406 ± 0.00787	0.82845 ± 0.00135	0.51385 ± 0.00368	0.52555 ± 0.00591	16,286,753 ± 0.00000	8,700,046,055,998.40039 ± 19,108,366,057,049.83203	2,496.42857 ± 811.76486

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.78343 ± 0.00358	0.77472 ± 0.00317	0.75483 ± 0.00398	0.73756 ± 0.00460	0.79378 ± 0.01417	0.68917 ± 0.01544	0.82661 ± 0.00163	0.50848 ± 0.00248	0.51438 ± 0.00927	55,209 ± 0.00000	21,066,396,060.8 ± 33,262,000,708.4252	445.48115 ± 141.94076
IGTD	0.79121 ± 0.00452	0.78381 ± 0.00483	0.75472 ± 0.00525	0.73541 ± 0.00795	0.79818 ± 0.00664	0.68196 ± 0.01462	0.82477 ± 0.00422	0.51912 ± 0.00610	0.51500 ± 0.00967	1,726,097 ± 0.00000	35,408,168,048 ± 35,082,886,876.55688	770.37093 ± 265.31412
REFINED	0.79441 ± 0.00464	0.78392 ± 0.00285	0.75623 ± 0.00245	0.73979 ± 0.00437	0.79327 ± 0.00312	0.69313 ± 0.00919	0.82636 ± 0.00202	0.51456 ± 0.00652	0.51663 ± 0.00421	594,577 ± 0.00000	6,874,985,318.4 ± 10,840,271,169.55768	487.88913 ± 171.38286
DistanceMatrix	0.79528 ± 0.00807	0.78544 ± 0.00433	0.75448 ± 0.00728	0.73550 ± 0.00727	0.79721 ± 0.00999	0.68265 ± 0.00543	0.82276 ± 0.00141	0.52153 ± 0.00638	0.51431 ± 0.01502	47,329 ± 0.00000	125,413,487,040 ± 271,898,806,184.23105	696.09263 ± 221.51648
BarGraph	0.79818 ± 0.00615	0.78987 ± 0.00593	0.76112 ± 0.00766	0.74644 ± 0.00810	0.79567 ± 0.01647	0.70338 ± 0.01720	0.82965 ± 0.00462	0.50822 ± 0.00744	0.52604 ± 0.01620	1,246,593 ± 0.00000	143,853,906,534.39999 ± 267,314,456,779.50229	633.02015 ± 197.07502
Combination	0.79860 ± 0.00783	0.78952 ± 0.00182	0.76228 ± 0.00208	0.74667 ± 0.00537	0.79974 ± 0.01894	0.70105 ± 0.02342	0.83534 ± 0.00297	0.50503 ± 0.00498	0.52912 ± 0.00709	2,743,089 ± 0.00000	2,051,088,364,646.3999 ± 2,762,851,296,694.4126	711.06372 ± 197.85187
SuperTML	0.80142 ± 0.01416	0.78672 ± 0.00486	0.76496 ± 0.00412	0.75360 ± 0.00620	0.79230 ± 0.01688	0.71921 ± 0.02202	0.83152 ± 0.00320	0.51131 ± 0.01168	0.53263 ± 0.00931	655,073 ± 0.00000	58,837,451,833.6 ± 128,193,210,627.53494	1,431.14861 ± 148.66043
FeatureWrap	0.78507 ± 0.00438	0.77729 ± 0.00564	0.75448 ± 0.00457	0.73475 ± 0.00605	0.79935 ± 0.01627	0.68033 ± 0.01825	0.83036 ± 0.00136	0.50838 ± 0.00352	0.51501 ± 0.01073	26,873 ± 0.00000	670,474,232.8 ± 1,478,111,954.598	448.96570 ± 144.08506
BIE	0.78545 ± 0.00776	0.77822 ± 0.00735	0.75681 ± 0.00601	0.74026 ± 0.01121	0.79517 ± 0.02488	0.69406 ± 0.03449	0.82411 ± 0.00330	0.51322 ± 0.00500	0.51876 ± 0.01317	193,937 ± 0.00000	46,562,342,956.8 ± 101,317,122,478.31845	415.89008 ± 111.37664

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.79107 ± 0.00604	0.78125 ± 0.00421	0.75378 ± 0.00288	0.73228 ± 0.00313	0.80264 ± 0.01313	0.67357 ± 0.01314	0.82741 ± 0.00180	0.51278 ± 0.00632	0.51445 ± 0.00766	101,369 ± 0.00000	26,112,851,500.8 ± 56,223,615,942.36392	971.35377 ± 362.51331
IGTD	0.79743 ± 0.00296	0.78521 ± 0.00312	0.75320 ± 0.00632	0.73411 ± 0.00798	0.79558 ± 0.00554	0.68149 ± 0.01085	0.82428 ± 0.00306	0.51631 ± 0.00464	0.51171 ± 0.01223	1,742,513 ± 0.00000	84,773,405,875.2 ± 180,144,694,113.31934	1,624.47672 ± 618.84157
REFINED	0.79406 ± 0.00469	0.78206 ± 0.00437	0.75530 ± 0.00363	0.74004 ± 0.00273	0.78950 ± 0.01164	0.69662 ± 0.01030	0.82496 ± 0.00171	0.51333 ± 0.00362	0.51430 ± 0.00852	561,681 ± 0.00000	27,897,250,105.6 ± 59,931,665,658.70679	1,050.63638 ± 415.65452
DistanceMatrix	0.79296 ± 0.00719	0.78521 ± 0.00283	0.75693 ± 0.00360	0.73848 ± 0.00666	0.79912 ± 0.00710	0.68661 ± 0.01519	0.82477 ± 0.00161	0.52366 ± 0.00957	0.51914 ± 0.00630	107,201 ± 0.00000	241,645,307,878.39999 ± 519,791,690,920.10126	1,505.36997 ± 552.96056
BarGraph	0.80369 ± 0.00690	0.78789 ± 0.00649	0.76216 ± 0.00485	0.74884 ± 0.00624	0.79328 ± 0.00614	0.70920 ± 0.01096	0.83155 ± 0.00393	0.51126 ± 0.00697	0.52735 ± 0.00942	1,427,457 ± 0.00000	252,727,262,233.60001 ± 383,167,868,958.26599	1,434.95774 ± 526.65822
Combination	0.79481 ± 0.00555	0.78707 ± 0.00252	0.75658 ± 0.00865	0.74753 ± 0.00485	0.77841 ± 0.03227	0.72107 ± 0.03290	0.83076 ± 0.00301	0.51186 ± 0.00723	0.51589 ± 0.01978	3,186,481 ± 0.00000	4,843,369,695,616 ± 9,427,791,887,179.02344	1,489.01224 ± 502.87907
SuperTML	0.80574 ± 0.00960	0.78824 ± 0.00360	0.76577 ± 0.00374	0.75967 ± 0.00213	0.78027 ± 0.01195	0.74040 ± 0.01138	0.83457 ± 0.00086	0.51056 ± 0.00307	0.53242 ± 0.00816	718,977 ± 0.00000	26,027,278,931.2 ± 40,624,508,189.80072	1,488.9573 ± 134.62188
FeatureWrap	0.79226 ± 0.00438	0.78428 ± 0.00360	0.75390 ± 0.00374	0.73731 ± 0.00473	0.79057 ± 0.00329	0.69080 ± 0.00682	0.82511 ± 0.00417	0.52476 ± 0.00502	0.51190 ± 0.00722	86,425 ± 0.00000	631,268,136.8 ± 1,356,648,401.3246	515.40748 ± 169.17345
BIE	0.79411 ± 0.00909	0.78020 ± 0.00547	0.75728 ± 0.00359	0.75194 ± 0.00170	0.76904 ± 0.00980	0.73574 ± 0.00823	0.82298 ± 0.00244	0.51584 ± 0.00373	0.51514 ± 0.00761	283,089 ± 0.00000	64,517,300,620.8 ± 116,024,731,895.8228	510.11104 ± 144.47312

---

Nomao

Source: OpenML · Original

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	0.99959 ± 0.00013	0.96882 ± 0.00116	0.96866 ± 0.00072	0.97813 ± 0.00049	0.97534 ± 0.00120	0.98094 ± 0.00059	0.99455 ± 0.00018	0.09178 ± 0.00200	0.92294 ± 0.00183	88.42585 ± 12.09318	—	—
LightGBM	0.99996 ± 0.00000	0.96805 ± 0.00193	0.96905 ± 0.00168	0.97838 ± 0.00116	0.97643 ± 0.00203	0.98034 ± 0.00113	0.99446 ± 0.00027	0.12653 ± 0.00491	0.92397 ± 0.00418	2.86195 ± 0.74265	—	—
XGBoost	0.99996 ± 0.00000	0.97010 ± 0.00068	0.97033 ± 0.00082	0.97926 ± 0.00057	0.97796 ± 0.00083	0.98056 ± 0.00052	0.99467 ± 0.00018	0.16116 ± 0.00164	0.92717 ± 0.00202	7.27351 ± 0.40088	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.96946 ± 0.00181	0.95756 ± 0.00172	0.95722 ± 0.00156	0.97009 ± 0.00101	0.96886 ± 0.00418	0.97135 ± 0.00280	0.98967 ± 0.00032	0.11633 ± 0.00227	0.89502 ± 0.00424	77,825 ± 0.00000	155,264 ± 0.00000	104.73531 ± 8.62000

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.97018 ± 0.00175	0.95687 ± 0.00081	0.95408 ± 0.00145	0.96789 ± 0.00101	0.96703 ± 0.00287	0.96875 ± 0.00292	0.98818 ± 0.00027	0.12876 ± 0.00093	0.88738 ± 0.00360	20,832,601 ± 0.00000	321,724,384,026 ± 716,693,138,158.28979	479.35578 ± 132.34694
IGTD	0.96877 ± 0.00318	0.95609 ± 0.00148	0.95578 ± 0.00156	0.96893 ± 0.00109	0.97269 ± 0.00456	0.96523 ± 0.00454	0.98976 ± 0.00027	0.12060 ± 0.00220	0.89244 ± 0.00394	8,016,295 ± 0.00000	88,810,592,350 ± 196,489,694,368.91821	600.93735 ± 184.65323
REFINED	0.96974 ± 0.00262	0.95772 ± 0.00111	0.95776 ± 0.00143	0.97049 ± 0.00100	0.96852 ± 0.00309	0.97249 ± 0.00322	0.98975 ± 0.00041	0.11840 ± 0.00182	0.89624 ± 0.00352	6,170,215 ± 0.00000	48,069,892,848 ± 105,475,890,018.72287	273.23154 ± 93.66160
DistanceMatrix	0.97232 ± 0.00226	0.95830 ± 0.00156	0.95737 ± 0.00127	0.97011 ± 0.00085	0.97162 ± 0.00419	0.96864 ± 0.00385	0.98982 ± 0.00042	0.11436 ± 0.00176	0.89586 ± 0.00339	16,916,857 ± 0.00000	1,216,917,828,593.6001 ± 2,701,838,061,539.17529	2,003.17204 ± 442.38300
BarGraph	0.97770 ± 0.00259	0.96449 ± 0.00203	0.96317 ± 0.00115	0.97428 ± 0.00080	0.97193 ± 0.00220	0.97666 ± 0.00223	0.99197 ± 0.00024	0.10877 ± 0.00460	0.90949 ± 0.00285	14,940,295 ± 0.00000	2,729,093,792.8 ± 1,098,676,695.40115	1,823.59621 ± 426.87652
Combination	0.97471 ± 0.00235	0.96023 ± 0.00056	0.96054 ± 0.00141	0.97245 ± 0.00109	0.96991 ± 0.00366	0.97503 ± 0.00555	0.99146 ± 0.00036	0.10849 ± 0.00274	0.90309 ± 0.00299	18,955,015 ± 0.00000	324,311,687,748 ± 713,970,915,190.3125	1,817.10348 ± 424.34665
SuperTML	0.97414 ± 0.00229	0.95841 ± 0.00204	0.95834 ± 0.00202	0.97089 ± 0.00146	0.96906 ± 0.00417	0.97276 ± 0.00523	0.99021 ± 0.00054	0.11509 ± 0.00448	0.89774 ± 0.00483	29,687,425 ± 0.00000	463,065,373,002 ± 1,015,639,501,982.69202	2,317.6447 ± 598.48675
FeatureWrap	0.97463 ± 0.00264	0.95636 ± 0.00154	0.95385 ± 0.00159	0.96762 ± 0.00122	0.96977 ± 0.00259	0.96550 ± 0.00477	0.98942 ± 0.00042	0.12179 ± 0.00311	0.88739 ± 0.00324	16,593,433 ± 0.00000	14,186,899,825,777.59961 ± 31,523,326,885,631.57422	1,375.22182 ± 14.61001
BIE	0.98038 ± 0.00657	0.96012 ± 0.00249	0.96220 ± 0.00140	0.97368 ± 0.00093	0.96879 ± 0.00501	0.97866 ± 0.00451	0.99136 ± 0.00068	0.11296 ± 0.00429	0.90692 ± 0.00362	20,518,873 ± 0.00000	2,295,861,428.8 ± 466,543,598.43117	1,999.65134 ± 400.40337

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.96094 ± 0.00271	0.95079 ± 0.00089	0.94747 ± 0.00184	0.96252 ± 0.00136	0.98126 ± 0.00051	0.94449 ± 0.00249	0.98923 ± 0.00024	0.12866 ± 0.00120	0.87660 ± 0.00397	20,912,345 ± 0.00000	436,665,245,037.59998 ± 967,978,549,623.35474	536.51636 ± 122.00153
IGTD	0.95842 ± 0.00634	0.94770 ± 0.00439	0.94329 ± 0.00944	0.95929 ± 0.00722	0.98297 ± 0.00406	0.93691 ± 0.01733	0.98902 ± 0.00050	0.14727 ± 0.01828	0.86909 ± 0.01782	8,088,999 ± 0.00000	311,650,190,566.79999 ± 691,053,745,412.88818	609.21402 ± 184.93126
REFINED	0.95933 ± 0.00639	0.94553 ± 0.00425	0.94186 ± 0.00468	0.95825 ± 0.00360	0.98317 ± 0.00290	0.93463 ± 0.00926	0.98882 ± 0.00034	0.14336 ± 0.00848	0.86582 ± 0.00863	5,523,431 ± 0.00000	290,250,234,344.40002 ± 643,963,128,523.54883	310.17079 ± 98.09055
DistanceMatrix	0.96324 ± 0.00296	0.95249 ± 0.00219	0.95025 ± 0.00191	0.96463 ± 0.00141	0.97989 ± 0.00109	0.94985 ± 0.00285	0.98928 ± 0.00047	0.13087 ± 0.00376	0.88208 ± 0.00417	16,829,369 ± 0.00000	3,244,725,476.8 ± 614,858,240.92529	2,092.59517 ± 441.59267
BarGraph	0.97189 ± 0.00509	0.95783 ± 0.00473	0.95486 ± 0.00401	0.96791 ± 0.00305	0.98262 ± 0.00345	0.95370 ± 0.00890	0.99149 ± 0.00041	0.12608 ± 0.01130	0.89311 ± 0.00773	15,036,711 ± 0.00000	5,859,842,658.4 ± 1,105,429,326.47811	1,871.91148 ± 411.84487
Combination	0.97631 ± 0.00380	0.96116 ± 0.00233	0.96108 ± 0.00115	0.97288 ± 0.00092	0.96838 ± 0.00441	0.97747 ± 0.00597	0.99197 ± 0.00021	0.11065 ± 0.00206	0.90424 ± 0.00238	19,034,759 ± 0.00000	14,574,778,889 ± 29,109,194,669.32748	1,889.06406 ± 414.40158
SuperTML	0.97175 ± 0.00231	0.96039 ± 0.00149	0.95745 ± 0.00219	0.97026 ± 0.00148	0.96872 ± 0.00491	0.97184 ± 0.00394	0.99026 ± 0.00038	0.11335 ± 0.00316	0.89557 ± 0.00561	28,886,721 ± 0.00000	4,418,466,958 ± 2,306,385,594.11359	2,432.58342 ± 602.41558
FeatureWrap	0.96767 ± 0.00320	0.95787 ± 0.00098	0.95729 ± 0.00131	0.97018 ± 0.00091	0.96770 ± 0.00375	0.97271 ± 0.00381	0.98966 ± 0.00036	0.11717 ± 0.00241	0.89505 ± 0.00335	16,689,785 ± 0.00000	10,110,585,872,880.80078 ± 22,502,629,053,303.78906	1,459.74151 ± 16.63757
BIE	0.97529 ± 0.00112	0.96039 ± 0.00146	0.96174 ± 0.00318	0.97328 ± 0.00219	0.97107 ± 0.00413	0.97552 ± 0.00271	0.99145 ± 0.00079	0.10494 ± 0.00498	0.90599 ± 0.00798	19,834,905 ± 0.00000	212,099,541,471.20001 ± 466,396,281,712.28033	2,028.60861 ± 450.41631

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.97399 ± 0.00341	0.95791 ± 0.00113	0.95834 ± 0.00131	0.97091 ± 0.00090	0.96850 ± 0.00407	0.97335 ± 0.00402	0.98945 ± 0.00015	0.11745 ± 0.00177	0.89763 ± 0.00339	4,824,641 ± 0.00000	934,910,685,593.59998 ± 1,951,421,727,771.86011	248.10662 ± 49.49744
IGTD	0.97955 ± 0.00127	0.95872 ± 0.00176	0.94619 ± 0.00992	0.96205 ± 0.00776	0.96697 ± 0.01112	0.95770 ± 0.02445	0.98718 ± 0.00128	0.14034 ± 0.02637	0.87079 ± 0.01815	1,189,033 ± 0.00000	45,912,542,356.8 ± 100,013,680,314.8199	229.56258 ± 75.03398
REFINED	0.97595 ± 0.00309	0.95733 ± 0.00159	0.96027 ± 0.00180	0.97231 ± 0.00119	0.96823 ± 0.00414	0.97644 ± 0.00276	0.99061 ± 0.00057	0.11056 ± 0.00417	0.90217 ± 0.00466	5,396,049 ± 0.00000	347,208,752,755.20001 ± 436,028,655,509.88867	271.48560 ± 87.74921
DistanceMatrix	0.98507 ± 0.00388	0.96220 ± 0.00180	0.95996 ± 0.00270	0.97201 ± 0.00194	0.97062 ± 0.00553	0.97346 ± 0.00678	0.99125 ± 0.00093	0.11265 ± 0.00536	0.90186 ± 0.00639	2,551,521 ± 0.00000	2,950,933,827,148.7998 ± 5,449,995,671,441.93066	7,836.87877 ± 368.73048
BarGraph	0.99080 ± 0.00983	0.96437 ± 0.00295	0.96174 ± 0.00237	0.97326 ± 0.00174	0.97158 ± 0.00309	0.97498 ± 0.00542	0.99228 ± 0.00074	0.10184 ± 0.00637	0.90612 ± 0.00545	2,700,657 ± 0.00000	135,052,239,059.2 ± 83,035,178,316.82898	2,877.01942 ± 392.42460
Combination	0.99160 ± 0.00358	0.96499 ± 0.00184	0.96414 ± 0.00246	0.97496 ± 0.00162	0.97297 ± 0.00605	0.97698 ± 0.00371	0.99320 ± 0.00053	0.09610 ± 0.00439	0.91196 ± 0.00645	377,657 ± 0.00000	200,661,199,420.79999 ± 382,097,593,437.39655	4,090.50706 ± 372.66968
SuperTML	0.99957 ± 0.00062	0.96913 ± 0.00188	0.96371 ± 0.00144	0.97466 ± 0.00102	0.97246 ± 0.00288	0.97688 ± 0.00331	0.99325 ± 0.00021	0.09397 ± 0.00214	0.91086 ± 0.00350	3,470,009 ± 0.00000	225,938,498,278.39999 ± 51,363,290,437.87039	5,664.23751 ± 603.52141
FeatureWrap	0.98454 ± 0.00460	0.95992 ± 0.00153	0.95532 ± 0.00118	0.96885 ± 0.00082	0.96508 ± 0.00214	0.97265 ± 0.00198	0.98911 ± 0.00030	0.11883 ± 0.00177	0.88998 ± 0.00298	2,675,753 ± 0.00000	615,941,707,929.59998 ± 1,234,939,973,572.32837	247.36853 ± 73.54608
BIE	0.99947 ± 0.00036	0.96778 ± 0.00294	0.96325 ± 0.00301	0.97436 ± 0.00211	0.97121 ± 0.00565	0.97758 ± 0.00614	0.99305 ± 0.00082	0.10405 ± 0.00443	0.90972 ± 0.00734	4,865,385 ± 0.00000	82,842,842,886.39999 ± 19,048,789,357.48722	5,278.22185 ± 377.79029

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.96902 ± 0.00397	0.95741 ± 0.00181	0.95536 ± 0.00228	0.96878 ± 0.00161	0.96780 ± 0.00309	0.96978 ± 0.00382	0.98840 ± 0.00122	0.12401 ± 0.00645	0.89050 ± 0.00552	4,944,961 ± 0.00000	1,009,951,291,769.59998 ± 2,250,904,739,374.57715	292.18680 ± 68.75808
IGTD	0.97907 ± 0.00356	0.95911 ± 0.00166	0.95222 ± 0.00260	0.96665 ± 0.00162	0.96425 ± 0.00779	0.96913 ± 0.00525	0.98833 ± 0.00069	0.12705 ± 0.00520	0.88267 ± 0.00725	1,176,537 ± 0.00000	34,373,092,035.2 ± 74,197,311,611.16965	289.74496 ± 97.68266
REFINED	0.98069 ± 0.00354	0.96043 ± 0.00074	0.96066 ± 0.00107	0.97250 ± 0.00067	0.97115 ± 0.00492	0.97390 ± 0.00422	0.99099 ± 0.00039	0.11247 ± 0.00182	0.90349 ± 0.00302	4,991,057 ± 0.00000	687,744,956,620.80005 ± 983,491,552,191.08459	351.81945 ± 118.21687
DistanceMatrix	0.98581 ± 0.00299	0.96298 ± 0.00188	0.96182 ± 0.00117	0.97329 ± 0.00083	0.97284 ± 0.00545	0.97379 ± 0.00574	0.99216 ± 0.00067	0.10285 ± 0.00299	0.90652 ± 0.00298	2,481,313 ± 0.00000	39,888,640,966,214.39844 ± 87,267,777,539,044.9375	7,832.55724 ± 364.07442
BarGraph	0.99333 ± 0.00371	0.96518 ± 0.00120	0.96356 ± 0.00130	0.97455 ± 0.00090	0.97247 ± 0.00366	0.97666 ± 0.00370	0.99210 ± 0.00042	0.10200 ± 0.00418	0.91051 ± 0.00330	2,778,481 ± 0.00000	192,841,363,641.60001 ± 60,978,589,927.0471	2,880.8014 ± 398.38785
Combination	0.99374 ± 0.00233	0.96735 ± 0.00228	0.96503 ± 0.00104	0.97554 ± 0.00074	0.97470 ± 0.00547	0.97644 ± 0.00579	0.99383 ± 0.00047	0.09629 ± 0.00476	0.91435 ± 0.00259	455,481 ± 0.00000	129,644,373,555.2 ± 42,081,651,568.24149	4,018.11123 ± 361.36135
SuperTML	0.99523 ± 0.00204	0.96665 ± 0.00138	0.96062 ± 0.00211	0.97262 ± 0.00134	0.96623 ± 0.00643	0.97915 ± 0.00429	0.99199 ± 0.00060	0.10162 ± 0.00422	0.90289 ± 0.00558	3,560,793 ± 0.00000	751,565,383,718.40002 ± 241,897,377,555.09045	5,590.31739 ± 596.28220
FeatureWrap	0.98241 ± 0.00236	0.95954 ± 0.00085	0.95563 ± 0.00122	0.96896 ± 0.00085	0.96842 ± 0.00220	0.96951 ± 0.00220	0.98960 ± 0.00047	0.11845 ± 0.00304	0.89121 ± 0.00304	3,016,233 ± 0.00000	1,111,128,403,916.80005 ± 2,458,531,738,437.63916	489.89216 ± 177.26971
BIE	0.99982 ± 0.00012	0.96921 ± 0.00111	0.96549 ± 0.00095	0.97591 ± 0.00063	0.97355 ± 0.00259	0.97828 ± 0.00178	0.99383 ± 0.00052	0.09316 ± 0.00474	0.91519 ± 0.00252	4,943,209 ± 0.00000	263,538,065,689.60001 ± 148,660,074,826.40341	5,218.75606 ± 376.15809

---

Preprocessed heloc

Source: OpenML · FICO

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	0.81534 ± 0.00123	0.71452 ± 0.00187	0.73018 ± 0.00155	0.71293 ± 0.00111	0.72758 ± 0.00279	0.69887 ± 0.00154	0.79083 ± 0.00025	0.55470 ± 0.00032	0.45894 ± 0.00310	12.60065 ± 0.80073	—	—
LightGBM	0.81080 ± 0.00145	0.71938 ± 0.00140	0.72964 ± 0.00322	0.71334 ± 0.00345	0.72539 ± 0.00349	0.70169 ± 0.00365	0.78875 ± 0.00157	0.55797 ± 0.00217	0.45789 ± 0.00647	1.06779 ± 0.02788	—	—
XGBoost	0.80266 ± 0.00124	0.71533 ± 0.00037	0.73153 ± 0.00194	0.71321 ± 0.00245	0.73093 ± 0.00165	0.69634 ± 0.00339	0.79220 ± 0.00038	0.55225 ± 0.00045	0.46162 ± 0.00391	1.21999 ± 0.03794	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.74086 ± 0.00411	0.70939 ± 0.00346	0.71924 ± 0.00300	0.70771 ± 0.00251	0.70657 ± 0.00763	0.70901 ± 0.01038	0.78686 ± 0.00112	0.55979 ± 0.00170	0.43772 ± 0.00560	275,457 ± 0.00000	549,888 ± 0.00000	27.11040 ± 3.04776

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.73189 ± 0.00614	0.71060 ± 0.00194	0.70385 ± 0.00423	0.69168 ± 0.00179	0.69073 ± 0.01107	0.69296 ± 0.01306	0.77142 ± 0.00198	0.57640 ± 0.00316	0.40697 ± 0.00786	3,652,951 ± 0.00000	126,416,850 ± 0.00000	246.38582 ± 7.80233
REFINED	0.74723 ± 0.00783	0.72046 ± 0.00419	0.72167 ± 0.00303	0.70543 ± 0.01166	0.71610 ± 0.01037	0.69606 ± 0.03166	0.78639 ± 0.00293	0.56040 ± 0.00353	0.44248 ± 0.00641	16,030,983 ± 0.00000	835,827,367 ± 0.00000	239.63486 ± 0.81967
DistanceMatrix	0.75009 ± 0.00436	0.71182 ± 0.00495	0.72303 ± 0.00530	0.71035 ± 0.00855	0.71273 ± 0.01586	0.70901 ± 0.02836	0.78008 ± 0.00409	0.56678 ± 0.00424	0.44561 ± 0.01073	11,227,553 ± 0.00000	112,114,696 ± 0.00000	232.46146 ± 1.59433
BarGraph	0.74949 ± 0.00270	0.71816 ± 0.00200	0.72856 ± 0.00239	0.71816 ± 0.00207	0.71505 ± 0.00609	0.72141 ± 0.00824	0.78951 ± 0.00144	0.55943 ± 0.00256	0.45648 ± 0.00441	8,749,441 ± 0.00000	170,357,984 ± 0.00000	140.40542 ± 0.97042
Combination	0.74370 ± 0.00339	0.72019 ± 0.00390	0.72424 ± 0.00340	0.70820 ± 0.01185	0.71976 ± 0.02327	0.69944 ± 0.04327	0.78571 ± 0.00210	0.56378 ± 0.00389	0.44850 ± 0.00622	22,121,113 ± 0.00000	731,558,160 ± 0.00000	265.40014 ± 1.80066
SuperTML	0.74462 ± 0.00302	0.71573 ± 0.00253	0.71222 ± 0.00415	0.70539 ± 0.01196	0.69297 ± 0.01381	0.71972 ± 0.03696	0.78436 ± 0.00283	0.56604 ± 0.00366	0.42549 ± 0.00844	4,226,689 ± 0.00000	34,953,804 ± 0.00000	294.30081 ± 4.58905
FeatureWrap	0.73070 ± 0.00743	0.69602 ± 0.00967	0.68602 ± 0.01163	0.60964 ± 0.03549	0.75465 ± 0.02510	0.51465 ± 0.05618	0.75080 ± 0.00370	0.60916 ± 0.01238	0.38279 ± 0.01778	7,621,953 ± 0.00000	74,702,240 ± 0.00000	198.13757 ± 1.54118
BIE	0.74323 ± 0.00552	0.70993 ± 0.00401	0.70939 ± 0.00418	0.69029 ± 0.00876	0.70647 ± 0.01856	0.67634 ± 0.03403	0.77499 ± 0.00432	0.57229 ± 0.00477	0.41803 ± 0.00763	21,504,097 ± 0.00000	718,141,612 ± 0.00000	275.59880 ± 0.41604

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.75018 ± 0.00550	0.71168 ± 0.00559	0.71965 ± 0.00329	0.70459 ± 0.00475	0.71195 ± 0.00474	0.69747 ± 0.01008	0.78619 ± 0.00211	0.56072 ± 0.00448	0.43803 ± 0.00659	4,365,975 ± 0.00000	1,260,800,912 ± 249,187,659.8711	271.84837 ± 4.91254
IGTD	0.74842 ± 0.00453	0.71249 ± 0.00535	0.72330 ± 0.00318	0.71260 ± 0.01017	0.71039 ± 0.01906	0.71662 ± 0.03720	0.78636 ± 0.00235	0.56076 ± 0.00270	0.44690 ± 0.00519	3,802,695 ± 0.00000	3,641,537,502 ± 319,876,463.64556	124.45561 ± 1.42195
REFINED	0.74792 ± 0.00437	0.72073 ± 0.00289	0.72613 ± 0.00148	0.71400 ± 0.00383	0.71500 ± 0.00683	0.71324 ± 0.01386	0.78765 ± 0.00110	0.56077 ± 0.00337	0.45142 ± 0.00275	16,578,023 ± 0.00000	7,724,505,912 ± 1,526,689,530.08476	307.87920 ± 5.66926
DistanceMatrix	0.74321 ± 0.00476	0.70993 ± 0.00393	0.72046 ± 0.00282	0.70510 ± 0.00890	0.71420 ± 0.02035	0.69803 ± 0.03661	0.78704 ± 0.00253	0.55945 ± 0.00222	0.44063 ± 0.00487	11,758,017 ± 0.00000	1,613,907,008 ± 318,976,379.81172	271.26229 ± 3.63171
BarGraph	0.74596 ± 0.00200	0.71600 ± 0.00264	0.72262 ± 0.00585	0.71318 ± 0.00632	0.70727 ± 0.00958	0.71944 ± 0.01431	0.78838 ± 0.00196	0.55868 ± 0.00227	0.44489 ± 0.01145	8,791,489 ± 0.00000	1,906,052,864 ± 376,716,774.43295	161.77120 ± 3.03141
Combination	0.74902 ± 0.00614	0.71789 ± 0.00307	0.72059 ± 0.00417	0.71308 ± 0.00592	0.70250 ± 0.01088	0.72451 ± 0.02052	0.78417 ± 0.00110	0.56429 ± 0.00147	0.44146 ± 0.00785	21,657,369 ± 0.00000	7,228,835,456 ± 1,428,724,054.47137	319.35856 ± 3.76759
SuperTML	0.75261 ± 0.00692	0.71708 ± 0.00165	0.72167 ± 0.00175	0.71055 ± 0.01137	0.70912 ± 0.01758	0.71380 ± 0.03915	0.79104 ± 0.00086	0.55705 ± 0.00184	0.44362 ± 0.00298	4,795,617 ± 0.00000	716,128,920 ± 62,905,513.48657	294.52168 ± 10.56306
FeatureWrap	0.75148 ± 0.00220	0.71479 ± 0.00429	0.72194 ± 0.00288	0.70130 ± 0.01050	0.72330 ± 0.01384	0.68169 ± 0.03108	0.78393 ± 0.00221	0.56229 ± 0.00166	0.44295 ± 0.00534	7,847,297 ± 0.00000	1,075,005,824 ± 212,466,681.36163	240.10630 ± 3.00511
BIE	0.75198 ± 0.00303	0.71182 ± 0.00267	0.71830 ± 0.00406	0.70325 ± 0.00526	0.71089 ± 0.01528	0.69662 ± 0.02377	0.78720 ± 0.00330	0.56055 ± 0.00316	0.43577 ± 0.00746	21,382,337 ± 0.00000	7,077,399,136 ± 1,398,793,823.74174	340.72954 ± 5.72634

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.73852 ± 0.00634	0.71249 ± 0.00620	0.70749 ± 0.00639	0.69137 ± 0.00852	0.69972 ± 0.01131	0.68366 ± 0.01986	0.77448 ± 0.00169	0.57122 ± 0.00149	0.41381 ± 0.01271	278,225 ± 0.00000	37,160,128 ± 0.00000	113.83074 ± 3.59936
IGTD	0.71573 ± 0.00507	0.52384 ± 0.00876	0.60230 ± 0.05338	0.31990 ± 0.18837	0.83421 ± 0.01942	0.21493 ± 0.14625	0.76311 ± 0.00692	0.66649 ± 0.00670	0.25297 ± 0.10909	1,356,865 ± 0.00000	24,724,160 ± 0.00000	63.25363 ± 0.26247
REFINED	0.75296 ± 0.00231	0.71708 ± 0.00435	0.72451 ± 0.00327	0.70942 ± 0.01123	0.71803 ± 0.01686	0.70254 ± 0.03596	0.78669 ± 0.00416	0.56117 ± 0.00429	0.44858 ± 0.00721	796,041 ± 0.00000	14,232,096 ± 0.00000	165.31761 ± 0.96082
DistanceMatrix	0.74703 ± 0.00930	0.71425 ± 0.00425	0.72073 ± 0.00613	0.70170 ± 0.01133	0.71945 ± 0.01534	0.68592 ± 0.03149	0.78406 ± 0.00406	0.56305 ± 0.00668	0.44055 ± 0.01177	1,149,929 ± 0.00000	177,281,304 ± 0.00000	75.59714 ± 0.54278
BarGraph	0.75985 ± 0.01265	0.71209 ± 0.00511	0.71519 ± 0.01434	0.69929 ± 0.02890	0.70812 ± 0.02192	0.69465 ± 0.06668	0.78234 ± 0.00528	0.57148 ± 0.01425	0.43118 ± 0.02807	4,560,465 ± 0.00000	698,342,656 ± 0.00000	128.64228 ± 4.40663
Combination	0.74876 ± 0.00342	0.71695 ± 0.00495	0.72113 ± 0.00534	0.70105 ± 0.01535	0.72117 ± 0.01167	0.68338 ± 0.03849	0.78389 ± 0.00117	0.56961 ± 0.00628	0.44144 ± 0.01061	3,038,273 ± 0.00000	475,324,672 ± 0.00000	96.89109 ± 1.28411
SuperTML	0.76008 ± 0.00571	0.72762 ± 0.00297	0.71735 ± 0.00627	0.70117 ± 0.00717	0.71105 ± 0.01018	0.69183 ± 0.01515	0.78645 ± 0.00309	0.55941 ± 0.00324	0.43344 ± 0.01240	536,857 ± 0.00000	448,495,408 ± 0.00000	392.56344 ± 14.20557
FeatureWrap	0.73229 ± 0.01224	0.69372 ± 0.00913	0.68589 ± 0.02771	0.61307 ± 0.08937	0.74655 ± 0.04798	0.54000 ± 0.14382	0.75923 ± 0.00959	0.60747 ± 0.01968	0.38426 ± 0.03925	411,345 ± 0.00000	7,647,744 ± 0.00000	79.77829 ± 0.69233
BIE	0.75021 ± 0.01039	0.70587 ± 0.00798	0.70709 ± 0.00667	0.67746 ± 0.02397	0.71782 ± 0.02007	0.64479 ± 0.05931	0.77332 ± 0.00357	0.57550 ± 0.00433	0.41450 ± 0.01299	341,729 ± 0.00000	206,823,744 ± 0.00000	136.01211 ± 0.56600

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.74624 ± 0.00889	0.71222 ± 0.00801	0.71344 ± 0.00636	0.70164 ± 0.00976	0.70162 ± 0.02283	0.70394 ± 0.04204	0.77794 ± 0.00491	0.57164 ± 0.00532	0.42742 ± 0.01158	651,793 ± 0.00000	37,905,920 ± 0.00000	140.01672 ± 15.43977
IGTD	0.71452 ± 0.00546	0.52221 ± 0.00322	0.57583 ± 0.06515	0.21285 ± 0.24229	0.69059 ± 0.38899	0.14620 ± 0.17598	0.76697 ± 0.00577	0.66653 ± 0.00731	0.17729 ± 0.15261	1,762,881 ± 0.00000	25,535,168 ± 0.00000	68.83911 ± 0.24535
REFINED	0.74584 ± 0.00558	0.71735 ± 0.00624	0.71911 ± 0.00874	0.70675 ± 0.01204	0.70749 ± 0.01084	0.70648 ± 0.02383	0.78634 ± 0.00521	0.56240 ± 0.00557	0.43749 ± 0.01754	1,255,689 ± 0.00000	15,150,144 ± 0.00000	165.45272 ± 0.39471
DistanceMatrix	0.75565 ± 0.00816	0.71101 ± 0.00460	0.72330 ± 0.00193	0.71380 ± 0.00481	0.70810 ± 0.00876	0.72000 ± 0.01862	0.78443 ± 0.00149	0.56410 ± 0.00153	0.44634 ± 0.00346	1,442,697 ± 0.00000	177,865,984 ± 0.00000	81.03928 ± 1.43009
BarGraph	0.76289 ± 0.00613	0.71344 ± 0.00897	0.71992 ± 0.00582	0.70786 ± 0.01256	0.70807 ± 0.01222	0.70873 ± 0.03290	0.78508 ± 0.00268	0.56360 ± 0.00460	0.43951 ± 0.01190	4,625,681 ± 0.00000	698,472,704 ± 0.00000	132.94448 ± 3.62290
Combination	0.75548 ± 0.00365	0.71654 ± 0.00216	0.71789 ± 0.00636	0.70241 ± 0.01238	0.71114 ± 0.01823	0.69549 ± 0.03671	0.78277 ± 0.00465	0.56702 ± 0.00543	0.43534 ± 0.01221	3,103,489 ± 0.00000	475,454,720 ± 0.00000	106.05786 ± 5.47090
SuperTML	0.75554 ± 0.00903	0.71816 ± 0.00418	0.71978 ± 0.00274	0.70065 ± 0.00937	0.71852 ± 0.01520	0.68479 ± 0.03070	0.78556 ± 0.00343	0.56152 ± 0.00433	0.43866 ± 0.00577	820,377 ± 0.00000	449,061,536 ± 0.00000	385.57753 ± 17.30651
FeatureWrap	0.75843 ± 0.01048	0.70817 ± 0.00572	0.71236 ± 0.00795	0.68095 ± 0.02661	0.72714 ± 0.01910	0.64366 ± 0.05901	0.77364 ± 0.00599	0.57601 ± 0.01118	0.42524 ± 0.01285	979,889 ± 0.00000	8,783,488 ± 0.00000	82.89502 ± 0.41953
BIE	0.75554 ± 0.01022	0.70952 ± 0.00401	0.71951 ± 0.00599	0.69518 ± 0.01190	0.72585 ± 0.01362	0.66789 ± 0.02955	0.78493 ± 0.00434	0.56597 ± 0.00839	0.43807 ± 0.01232	592,481 ± 0.00000	207,324,416 ± 0.00000	139.29321 ± 0.56451

---

Qsar-biodeg

Source: OpenML · Original

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	1.00000 ± 0.00000	0.84937 ± 0.00825	0.84528 ± 0.00954	0.77345 ± 0.01441	0.76930 ± 0.01419	0.77778 ± 0.01852	0.90328 ± 0.00679	0.68418 ± 0.02481	0.65608 ± 0.02144	2.07775 ± 0.02123	—	—
LightGBM	0.99594 ± 0.00096	0.85190 ± 0.00566	0.83899 ± 0.00954	0.76035 ± 0.01288	0.76912 ± 0.01746	0.75185 ± 0.01014	0.89986 ± 0.00490	0.41587 ± 0.00885	0.63928 ± 0.02045	0.42185 ± 0.01293	—	—
XGBoost	0.99837 ± 0.00061	0.86456 ± 0.00722	0.83899 ± 0.00345	0.76536 ± 0.01024	0.75775 ± 0.01263	0.77407 ± 0.03043	0.90197 ± 0.00668	0.46418 ± 0.02441	0.64342 ± 0.01095	0.36044 ± 0.01231	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.88347 ± 0.00991	0.84304 ± 0.01812	0.81384 ± 0.01699	0.71227 ± 0.04256	0.74727 ± 0.02097	0.68519 ± 0.08385	0.90547 ± 0.00199	0.36894 ± 0.00316	0.57886 ± 0.04706	76,801 ± 0.00000	153,088 ± 0.00000	7.07626 ± 0.03709

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.91138 ± 0.02023	0.84810 ± 0.01733	0.81887 ± 0.02197	0.71728 ± 0.03638	0.76268 ± 0.03440	0.67778 ± 0.04648	0.89160 ± 0.00666	0.43290 ± 0.03698	0.58735 ± 0.05142	677,719 ± 0.00000	23,443,743 ± 0.00000	29.58682 ± 0.30404
IGTD	0.66260 ± 0.00000	0.66456 ± 0.00000	0.66038 ± 0.00000	0.00000 ± 0.00000	0.00000 ± 0.00000	0.00000 ± 0.00000	0.50000 ± 0.00000	0.64079 ± 0.00000	0.00000 ± 0.00000	19,943,815 ± 0.00000	2,020,915,039 ± 0.00000	61.29744 ± 0.30382
REFINED	0.92222 ± 0.02092	0.84304 ± 0.00825	0.83648 ± 0.02476	0.75538 ± 0.03627	0.77030 ± 0.05212	0.74444 ± 0.05617	0.89901 ± 0.00300	0.42438 ± 0.04314	0.63457 ± 0.05425	10,065,543 ± 0.00000	1,013,835,391 ± 0.00000	40.20941 ± 1.32542
DistanceMatrix	0.88889 ± 0.01597	0.85316 ± 0.00693	0.80629 ± 0.02057	0.70298 ± 0.02562	0.73682 ± 0.05021	0.67407 ± 0.03099	0.90148 ± 0.01580	0.38558 ± 0.02197	0.56191 ± 0.04381	11,380,375 ± 0.00000	4,682,747,412 ± 0.00000	112.79609 ± 0.10942
BarGraph	0.90786 ± 0.01845	0.84684 ± 0.02255	0.84151 ± 0.02197	0.76166 ± 0.04362	0.77426 ± 0.01207	0.75185 ± 0.07476	0.90833 ± 0.00608	0.40301 ± 0.02131	0.64441 ± 0.05562	17,078,137 ± 0.00000	332,845,900 ± 0.00000	56.28975 ± 2.26031
Combination	0.92439 ± 0.01280	0.87342 ± 0.01343	0.85031 ± 0.00689	0.78288 ± 0.01555	0.77149 ± 0.01774	0.79630 ± 0.04141	0.91933 ± 0.00531	0.38932 ± 0.03501	0.66980 ± 0.01812	11,376,727 ± 0.00000	111,112,224 ± 0.00000	35.89860 ± 0.99466
SuperTML	0.89295 ± 0.02247	0.84557 ± 0.01650	0.80503 ± 0.01938	0.71044 ± 0.05840	0.71352 ± 0.02496	0.71852 ± 0.11814	0.87637 ± 0.00247	0.44840 ± 0.02651	0.56902 ± 0.05827	23,309,025 ± 0.00000	202,703,164 ± 0.00000	50.94762 ± 0.65236
FeatureWrap	0.85312 ± 0.00860	0.80886 ± 0.00530	0.76352 ± 0.03003	0.59579 ± 0.05415	0.71116 ± 0.06438	0.51482 ± 0.06198	0.84804 ± 0.02000	0.47564 ± 0.02456	0.44718 ± 0.07381	2,478,817 ± 0.00000	24,855,412 ± 0.00000	26.06886 ± 0.06475
BIE	0.88889 ± 0.01472	0.83418 ± 0.02300	0.80629 ± 0.02105	0.70658 ± 0.04614	0.72470 ± 0.02195	0.69259 ± 0.07922	0.89704 ± 0.00955	0.40296 ± 0.01374	0.56418 ± 0.05600	5,306,497 ± 0.00000	180,026,090 ± 0.00000	18.89083 ± 0.42705

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.89485 ± 0.01734	0.84304 ± 0.02948	0.82893 ± 0.01210	0.73164 ± 0.02632	0.78197 ± 0.01673	0.68889 ± 0.04793	0.89908 ± 0.00510	0.41228 ± 0.01391	0.61026 ± 0.03068	789,431 ± 0.00000	234,662,968 ± 46,379,341.33595	35.02912 ± 0.62252
IGTD	0.89756 ± 0.00618	0.86203 ± 0.00530	0.83145 ± 0.02328	0.74401 ± 0.04415	0.76464 ± 0.01701	0.72593 ± 0.06854	0.90638 ± 0.00330	0.36973 ± 0.00881	0.61976 ± 0.05779	19,841,031 ± 0.00000	17,493,490,936 ± 3,457,454,724.07943	68.65184 ± 0.32894
REFINED	0.92114 ± 0.02938	0.84810 ± 0.02146	0.84906 ± 0.02311	0.77265 ± 0.03966	0.78935 ± 0.03173	0.75926 ± 0.06677	0.89968 ± 0.00773	0.42395 ± 0.03069	0.66142 ± 0.05399	10,381,671 ± 0.00000	19,727,353,710 ± 1,732,871,386.98258	52.78226 ± 3.35506
DistanceMatrix	0.89377 ± 0.01043	0.86203 ± 0.01372	0.82767 ± 0.01757	0.74353 ± 0.02920	0.75177 ± 0.03060	0.73704 ± 0.04793	0.90356 ± 0.01117	0.39109 ± 0.02854	0.61471 ± 0.04150	11,647,287 ± 0.00000	38,262,608,032 ± 7,562,311,912.4608	117.23654 ± 0.77614
BarGraph	0.92629 ± 0.01210	0.86456 ± 0.01150	0.82138 ± 0.02206	0.72968 ± 0.04916	0.74668 ± 0.01975	0.71852 ± 0.09388	0.90406 ± 0.00769	0.42141 ± 0.02693	0.59939 ± 0.05981	17,072,217 ± 0.00000	12,966,251,088 ± 732,194,395.56636	75.38963 ± 8.44481
Combination	0.94065 ± 0.01339	0.86835 ± 0.01443	0.85535 ± 0.01722	0.78944 ± 0.02817	0.77949 ± 0.01839	0.80000 ± 0.04015	0.91799 ± 0.00117	0.40725 ± 0.02009	0.67962 ± 0.04063	11,315,031 ± 0.00000	3,535,396,992 ± 310,552,970.21189	43.58147 ± 1.86796
SuperTML	0.92520 ± 0.02326	0.84304 ± 0.01217	0.80000 ± 0.00820	0.68679 ± 0.03608	0.73332 ± 0.02467	0.65185 ± 0.08008	0.88487 ± 0.00168	0.43864 ± 0.02864	0.54580 ± 0.02822	22,928,993 ± 0.00000	7,786,935,184 ± 439,722,342.38468	66.24800 ± 6.86827
FeatureWrap	0.88780 ± 0.01107	0.83544 ± 0.01951	0.80503 ± 0.01177	0.68103 ± 0.02795	0.76538 ± 0.01704	0.61482 ± 0.04611	0.90952 ± 0.00729	0.37953 ± 0.02420	0.55057 ± 0.03017	2,917,409 ± 0.00000	360,110,752 ± 71,173,136.63975	35.18798 ± 0.53815
BIE	0.90190 ± 0.02154	0.84177 ± 0.01790	0.80252 ± 0.01142	0.70981 ± 0.02820	0.70830 ± 0.02201	0.71481 ± 0.06495	0.88381 ± 0.01224	0.44923 ± 0.00981	0.56200 ± 0.03313	5,614,369 ± 0.00000	1,759,626,448 ± 347,776,712.92199	28.88145 ± 0.39315

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.92276 ± 0.00796	0.86582 ± 0.00693	0.84780 ± 0.01745	0.76587 ± 0.03540	0.79931 ± 0.01773	0.73704 ± 0.06058	0.89157 ± 0.00584	0.38719 ± 0.00776	0.65561 ± 0.04349	766,225 ± 0.00000	30,574,976 ± 0.00000	25.09220 ± 1.22643
IGTD	0.88428 ± 0.03785	0.84430 ± 0.02393	0.82642 ± 0.03462	0.72275 ± 0.05636	0.78918 ± 0.05899	0.66667 ± 0.05399	0.89788 ± 0.02400	0.38704 ± 0.04211	0.60224 ± 0.08126	4,761,809 ± 0.00000	163,460,752 ± 0.00000	16.20256 ± 0.06821
REFINED	0.96287 ± 0.01078	0.84937 ± 0.01511	0.85031 ± 0.01434	0.77998 ± 0.02926	0.77678 ± 0.01077	0.78519 ± 0.05943	0.90589 ± 0.01199	0.39054 ± 0.03812	0.66766 ± 0.03799	9,909,553 ± 0.00000	340,932,336 ± 0.00000	25.17825 ± 0.46305
DistanceMatrix	0.93984 ± 0.02906	0.85190 ± 0.01930	0.84025 ± 0.01515	0.74686 ± 0.02954	0.80735 ± 0.02148	0.69630 ± 0.05003	0.91094 ± 0.00938	0.38867 ± 0.03748	0.63560 ± 0.03667	398,881 ± 0.00000	237,796,608 ± 0.00000	21.67408 ± 0.90089
BarGraph	0.93225 ± 0.01063	0.83671 ± 0.02343	0.84277 ± 0.02038	0.76706 ± 0.03815	0.76951 ± 0.02019	0.76667 ± 0.06495	0.90660 ± 0.00682	0.37349 ± 0.01086	0.64946 ± 0.05013	1,643,897 ± 0.00000	285,080,640 ± 0.00000	24.90927 ± 0.31574
Combination	0.98618 ± 0.00411	0.85316 ± 0.01638	0.84528 ± 0.01812	0.76814 ± 0.03445	0.78188 ± 0.03446	0.75926 ± 0.07052	0.90797 ± 0.00365	0.38541 ± 0.01811	0.65452 ± 0.04255	6,506,545 ± 0.00000	1,185,363,264 ± 0.00000	27.36433 ± 0.40257
SuperTML	0.90488 ± 0.01610	0.81139 ± 0.02508	0.77736 ± 0.02834	0.64904 ± 0.04761	0.69948 ± 0.05551	0.60741 ± 0.05768	0.85118 ± 0.02660	0.47525 ± 0.04847	0.49086 ± 0.06707	308,001 ± 0.00000	591,600,032 ± 0.00000	59.56045 ± 0.11764
FeatureWrap	0.85230 ± 0.01860	0.82278 ± 0.02571	0.81258 ± 0.03188	0.65190 ± 0.09313	0.86566 ± 0.02119	0.53333 ± 0.12519	0.90734 ± 0.00921	0.41628 ± 0.04282	0.56945 ± 0.07532	771,937 ± 0.00000	8,683,008 ± 0.00000	18.54361 ± 3.35914
BIE	0.92358 ± 0.04874	0.81646 ± 0.03319	0.77987 ± 0.01541	0.63687 ± 0.04206	0.72792 ± 0.05422	0.57407 ± 0.08282	0.84988 ± 0.02171	0.50347 ± 0.05394	0.49330 ± 0.03950	1,709,825 ± 0.00000	144,586,368 ± 0.00000	19.45627 ± 0.26663

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.94932 ± 0.01008	0.84430 ± 0.01878	0.85157 ± 0.01378	0.77328 ± 0.03488	0.80071 ± 0.01762	0.75185 ± 0.07361	0.88854 ± 0.00929	0.39206 ± 0.02848	0.66599 ± 0.03607	1,105,425 ± 0.00000	31,252,480 ± 0.00000	28.72899 ± 0.44864
IGTD	0.90407 ± 0.02783	0.85823 ± 0.02080	0.84906 ± 0.01541	0.77060 ± 0.03187	0.79640 ± 0.03748	0.75185 ± 0.07702	0.91083 ± 0.01195	0.37127 ± 0.01984	0.66188 ± 0.03869	5,363,153 ± 0.00000	164,662,160 ± 0.00000	17.62227 ± 0.66907
REFINED	0.98726 ± 0.00555	0.83924 ± 0.02351	0.85912 ± 0.01052	0.79213 ± 0.02105	0.79284 ± 0.01040	0.79259 ± 0.04421	0.90952 ± 0.00963	0.38927 ± 0.03677	0.68623 ± 0.02760	9,784,689 ± 0.00000	340,682,640 ± 0.00000	27.31155 ± 1.94910
DistanceMatrix	0.96423 ± 0.01633	0.85696 ± 0.00722	0.85157 ± 0.01812	0.78160 ± 0.03846	0.77956 ± 0.03137	0.78889 ± 0.08029	0.91778 ± 0.01068	0.37667 ± 0.03603	0.67195 ± 0.04565	487,713 ± 0.00000	237,973,696 ± 0.00000	25.62306 ± 0.40329
BarGraph	0.98591 ± 0.01207	0.83798 ± 0.00849	0.83648 ± 0.01334	0.76349 ± 0.02040	0.75063 ± 0.02432	0.77778 ± 0.03465	0.90106 ± 0.02035	0.38500 ± 0.03244	0.63936 ± 0.03037	1,711,929 ± 0.00000	285,216,384 ± 0.00000	29.47028 ± 0.50732
Combination	0.97344 ± 0.00805	0.86456 ± 0.02031	0.84403 ± 0.00689	0.76691 ± 0.02117	0.77830 ± 0.02188	0.75926 ± 0.06142	0.91298 ± 0.00702	0.36421 ± 0.01151	0.65172 ± 0.02346	6,443,313 ± 0.00000	1,185,236,736 ± 0.00000	31.55761 ± 0.85695
SuperTML	0.90190 ± 0.01529	0.82532 ± 0.01150	0.80503 ± 0.02395	0.67652 ± 0.05840	0.76916 ± 0.01546	0.60741 ± 0.08425	0.89048 ± 0.00405	0.42739 ± 0.02224	0.54946 ± 0.06066	401,441 ± 0.00000	591,786,384 ± 0.00000	68.14005 ± 1.34413
FeatureWrap	0.87886 ± 0.01160	0.82278 ± 0.03258	0.79874 ± 0.01258	0.64689 ± 0.02710	0.80384 ± 0.05650	0.54444 ± 0.04648	0.90152 ± 0.01347	0.40162 ± 0.01168	0.53404 ± 0.03323	805,537 ± 0.00000	8,749,968 ± 0.00000	19.23265 ± 1.01026
BIE	0.97073 ± 0.01768	0.83038 ± 0.02165	0.80880 ± 0.01699	0.70342 ± 0.02040	0.74527 ± 0.03833	0.66667 ± 0.01310	0.87478 ± 0.00864	0.42210 ± 0.01331	0.56522 ± 0.03576	1,792,161 ± 0.00000	144,750,720 ± 0.00000	23.14677 ± 0.18553

---

Sick

Source: OpenML · Original

Leaderboard

Architecture Results

Tree Baselines

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	Train time (s)	#Params	FLOPs
CatBoost	1.00000 ± 0.00000	0.98834 ± 0.00158	0.99329 ± 0.00148	0.94181 ± 0.01323	0.98124 ± 0.01714	0.90588 ± 0.02461	0.99974 ± 0.00016	0.01154 ± 0.00133	0.93923 ± 0.01374	12.15761 ± 0.10894	—	—
LightGBM	1.00000 ± 0.00000	0.98975 ± 0.00262	0.99011 ± 0.00158	0.91550 ± 0.01476	0.93823 ± 0.00174	0.89412 ± 0.02631	0.99853 ± 0.00025	0.02572 ± 0.00208	0.91063 ± 0.01518	117.51338 ± 5.29518	—	—
XGBoost	1.00000 ± 0.00000	0.98975 ± 0.00148	0.99470 ± 0.00000	0.95385 ± 0.00000	1.00000 ± 0.00000	0.91177 ± 0.00000	0.99956 ± 0.00006	0.01558 ± 0.00055	0.95218 ± 0.00000	1.35484 ± 0.10108	—	—

MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
MLP	0.97114 ± 0.00560	0.96643 ± 0.00250	0.95230 ± 0.00467	0.46935 ± 0.09472	0.69505 ± 0.04481	0.35882 ± 0.09160	0.94621 ± 0.00286	0.12325 ± 0.00276	0.47606 ± 0.08221	28,161 ± 0.00000	55,808 ± 0.00000	16.09173 ± 0.38074

ViT

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.96561 ± 0.00522	0.95901 ± 0.00290	0.94982 ± 0.00691	0.58199 ± 0.05247	0.58762 ± 0.06874	0.58235 ± 0.07323	0.96186 ± 0.00525	0.11067 ± 0.00933	0.55705 ± 0.05582	12,541,273 ± 0.00000	125,166,258 ± 0.00000	164.74915 ± 0.47587
IGTD	0.97856 ± 0.00504	0.97350 ± 0.00331	0.96148 ± 0.00383	0.69224 ± 0.05014	0.66437 ± 0.02961	0.72941 ± 0.09843	0.96866 ± 0.00795	0.10498 ± 0.01729	0.67441 ± 0.05194	2,706,017 ± 0.00000	26,246,106 ± 0.00000	94.31224 ± 0.81427
REFINED	0.99401 ± 0.00166	0.98834 ± 0.00321	0.99046 ± 0.00443	0.92076 ± 0.03709	0.91884 ± 0.04085	0.92353 ± 0.04460	0.99864 ± 0.00083	0.02541 ± 0.01088	0.91592 ± 0.03927	8,528,647 ± 0.00000	1,144,087,780 ± 0.00000	175.05356 ± 1.89928
DistanceMatrix	0.98796 ± 0.00488	0.98269 ± 0.00194	0.97774 ± 0.00493	0.79409 ± 0.06064	0.88157 ± 0.02536	0.72941 ± 0.10482	0.99180 ± 0.00316	0.05697 ± 0.00967	0.78870 ± 0.05550	10,990,567 ± 0.00000	803,164,072 ± 0.00000	199.69149 ± 4.58353
BarGraph	0.99076 ± 0.00292	0.98657 ± 0.00268	0.97279 ± 0.00158	0.75566 ± 0.01051	0.82180 ± 0.02991	0.70000 ± 0.01315	0.99180 ± 0.00089	0.06448 ± 0.00903	0.74423 ± 0.01240	9,863,047 ± 0.00000	730,349,156 ± 0.00000	190.97678 ± 3.42995
Combination	0.98901 ± 0.00587	0.98481 ± 0.00407	0.97208 ± 0.00383	0.75772 ± 0.01138	0.80640 ± 0.08622	0.72353 ± 0.05343	0.99151 ± 0.00076	0.07574 ± 0.01719	0.74700 ± 0.01622	21,612,007 ± 0.00000	1,606,355,748 ± 0.00000	231.62542 ± 5.14258
SuperTML	0.98674 ± 0.00431	0.98127 ± 0.00201	0.98905 ± 0.00521	0.91076 ± 0.03696	0.91608 ± 0.09248	0.91177 ± 0.03602	0.99792 ± 0.00087	0.02856 ± 0.00687	0.90673 ± 0.03945	2,752,961 ± 0.00000	362,439,712 ± 0.00000	121.46045 ± 0.67693
FeatureWrap	0.97917 ± 0.00206	0.96396 ± 0.00296	0.94770 ± 0.00387	0.38599 ± 0.10711	0.64200 ± 0.05985	0.28235 ± 0.09207	0.83982 ± 0.08735	0.17812 ± 0.01632	0.39960 ± 0.09043	14,503,321 ± 0.00000	744,533,868 ± 0.00000	173.84322 ± 6.30341
BIE	0.98644 ± 0.00270	0.96996 ± 0.00331	0.98587 ± 0.00331	0.88289 ± 0.02445	0.88452 ± 0.04933	0.88235 ± 0.00000	0.99329 ± 0.00156	0.04914 ± 0.00662	0.87568 ± 0.02634	6,779,137 ± 0.00000	223,694,368 ± 0.00000	152.71757 ± 2.49747

ViT + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.96796 ± 0.00333	0.96113 ± 0.00375	0.95795 ± 0.00521	0.60376 ± 0.04246	0.71474 ± 0.12739	0.53529 ± 0.07613	0.96194 ± 0.00776	0.10440 ± 0.00497	0.59327 ± 0.05036	12,599,865 ± 0.00000	1,798,167,824 ± 355,394,121.1918	195.16018 ± 10.17233
IGTD	0.98621 ± 0.00183	0.97632 ± 0.00158	0.96360 ± 0.00201	0.72676 ± 0.01440	0.66223 ± 0.01696	0.80588 ± 0.02631	0.96450 ± 0.00155	0.09834 ± 0.00827	0.71154 ± 0.01558	2,728,001 ± 0.00000	845,176,500 ± 74,241,187.91248	127.48017 ± 9.17736
REFINED	0.99492 ± 0.00297	0.98587 ± 0.00279	0.99046 ± 0.00237	0.92089 ± 0.01929	0.91832 ± 0.02342	0.92353 ± 0.01611	0.99753 ± 0.00105	0.02915 ± 0.00709	0.91583 ± 0.02052	9,115,335 ± 0.00000	9,736,695,328 ± 1,924,383,382.47501	188.65539 ± 4.48373
DistanceMatrix	0.98758 ± 0.00436	0.98339 ± 0.00268	0.97738 ± 0.00689	0.80839 ± 0.06465	0.82122 ± 0.06459	0.80000 ± 0.08921	0.99066 ± 0.00470	0.06305 ± 0.01414	0.79764 ± 0.06756	10,805,607 ± 0.00000	7,124,879,680 ± 1,408,177,990.21573	242.81214 ± 3.95959
BarGraph	0.98924 ± 0.00328	0.98657 ± 0.00201	0.97244 ± 0.00426	0.75713 ± 0.02390	0.81588 ± 0.08056	0.71176 ± 0.03835	0.99140 ± 0.00121	0.06079 ± 0.00375	0.74629 ± 0.02866	10,135,719 ± 0.00000	6,480,980,256 ± 1,280,916,192.47132	213.14444 ± 3.54831
Combination	0.98591 ± 0.00448	0.98092 ± 0.00230	0.97456 ± 0.00201	0.75848 ± 0.01708	0.88386 ± 0.03475	0.66471 ± 0.01611	0.99041 ± 0.00175	0.06439 ± 0.01303	0.75393 ± 0.01941	21,649,127 ± 0.00000	14,255,003,936 ± 2,817,392,530.77657	286.07178 ± 13.08104
SuperTML	0.99038 ± 0.00399	0.98021 ± 0.00403	0.98905 ± 0.00194	0.90557 ± 0.01815	0.93825 ± 0.02786	0.87647 ± 0.03835	0.99557 ± 0.00344	0.03629 ± 0.01205	0.90077 ± 0.01866	3,214,721 ± 0.00000	6,868,302,048 ± 603,318,836.93553	160.23175 ± 12.51680
FeatureWrap	0.97803 ± 0.00149	0.96290 ± 0.00216	0.94912 ± 0.00230	0.44157 ± 0.02069	0.65242 ± 0.05546	0.33529 ± 0.02631	0.88295 ± 0.02571	0.16716 ± 0.01349	0.44404 ± 0.02254	14,466,329 ± 0.00000	6,876,137,312 ± 1,359,015,963.12424	186.36146 ± 3.23081
BIE	0.98167 ± 0.00175	0.96855 ± 0.00230	0.97738 ± 0.00383	0.80320 ± 0.03661	0.83931 ± 0.02592	0.77059 ± 0.04833	0.98004 ± 0.00999	0.07353 ± 0.01193	0.79220 ± 0.03801	6,593,921 ± 0.00000	2,199,400,704 ± 434,694,732.00111	178.52272 ± 5.77908

CNN

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.97492 ± 0.00457	0.96360 ± 0.00097	0.95866 ± 0.00201	0.61074 ± 0.01889	0.70697 ± 0.04719	0.54118 ± 0.04460	0.96834 ± 0.00508	0.09965 ± 0.00561	0.59646 ± 0.01711	5,811,921 ± 0.00000	504,544,512 ± 0.00000	60.22975 ± 2.73501
IGTD	0.99091 ± 0.00348	0.97527 ± 0.00250	0.50989 ± 0.16878	0.19767 ± 0.04733	0.11196 ± 0.03183	0.93529 ± 0.09843	0.78446 ± 0.06959	1.17614 ± 0.32310	0.21019 ± 0.05187	430,409 ± 0.00000	7,845,504 ± 0.00000	53.34989 ± 3.99032
REFINED	0.98970 ± 0.00340	0.98269 ± 0.00340	0.97703 ± 0.00331	0.79811 ± 0.03202	0.84442 ± 0.03184	0.75882 ± 0.05658	0.98767 ± 0.00635	0.06148 ± 0.00356	0.78792 ± 0.03278	658,737 ± 0.00000	13,018,464 ± 0.00000	96.42864 ± 1.62120
DistanceMatrix	0.98901 ± 0.00303	0.98622 ± 0.00290	0.98551 ± 0.00422	0.87605 ± 0.03556	0.90410 ± 0.05371	0.85294 ± 0.05502	0.99623 ± 0.00100	0.04045 ± 0.00813	0.86975 ± 0.03660	284,897 ± 0.00000	266,999,040 ± 0.00000	87.48377 ± 1.62526
BarGraph	0.99242 ± 0.00346	0.97950 ± 0.00620	0.96678 ± 0.00194	0.72186 ± 0.01493	0.72710 ± 0.02439	0.71765 ± 0.02631	0.98576 ± 0.00057	0.08901 ± 0.01131	0.70449 ± 0.01561	2,767,521 ± 0.00000	905,616,000 ± 0.00000	73.37844 ± 0.77813
Combination	0.98682 ± 0.00745	0.97950 ± 0.00567	0.96855 ± 0.00422	0.72054 ± 0.03323	0.78245 ± 0.07852	0.67647 ± 0.07499	0.98586 ± 0.00553	0.08355 ± 0.00711	0.70892 ± 0.03554	1,903,353 ± 0.00000	517,387,616 ± 0.00000	68.39209 ± 1.78483
SuperTML	0.98955 ± 0.00346	0.98057 ± 0.00306	0.98905 ± 0.00230	0.90451 ± 0.02041	0.94837 ± 0.01767	0.86471 ± 0.02631	0.99735 ± 0.00054	0.03016 ± 0.00604	0.89985 ± 0.02141	161,177 ± 0.00000	259,514,168 ± 0.00000	128.05202 ± 1.17262
FeatureWrap	0.98129 ± 0.00375	0.96502 ± 0.00290	0.93322 ± 0.02122	0.49342 ± 0.05134	0.54033 ± 0.20622	0.54118 ± 0.15920	0.91895 ± 0.02492	0.16641 ± 0.04080	0.48445 ± 0.04361	307,489 ± 0.00000	10,907,392 ± 0.00000	44.15835 ± 2.44508
BIE	0.98803 ± 0.00675	0.97173 ± 0.00375	0.97703 ± 0.00780	0.79846 ± 0.08677	0.82651 ± 0.04313	0.78235 ± 0.14197	0.99110 ± 0.00299	0.06156 ± 0.01311	0.78966 ± 0.08635	440,337 ± 0.00000	572,670,432 ± 0.00000	94.26429 ± 0.14415

CNN + MLP

Method	Train Accuracy	Val Accuracy	Test Accuracy (↑)	Test F1 (↑)	Test Precision	Test Recall	Test ROC AUC	Test LogLoss	Test MCC	#Params	FLOPs	Train time (s)
TINTO	0.97667 ± 0.00598	0.96466 ± 0.00530	0.95760 ± 0.00515	0.57806 ± 0.04051	0.72617 ± 0.08716	0.48235 ± 0.03354	0.97090 ± 0.00732	0.09546 ± 0.00854	0.57047 ± 0.04841	4,789,969 ± 0.00000	502,501,632 ± 0.00000	55.14807 ± 0.76567
IGTD	0.98477 ± 0.00272	0.97668 ± 0.00403	0.65018 ± 0.24117	0.23966 ± 0.08294	0.16098 ± 0.07059	0.78235 ± 0.31582	0.83047 ± 0.07461	0.64664 ± 0.45625	0.24157 ± 0.09687	437,209 ± 0.00000	7,858,848 ± 0.00000	49.62886 ± 0.20061
REFINED	0.98614 ± 0.00577	0.98269 ± 0.00262	0.97456 ± 0.00509	0.79243 ± 0.03449	0.78682 ± 0.07332	0.80588 ± 0.06444	0.98720 ± 0.00641	0.06921 ± 0.01543	0.78114 ± 0.03563	805,297 ± 0.00000	13,311,104 ± 0.00000	82.11304 ± 2.82156
DistanceMatrix	0.98652 ± 0.00314	0.97950 ± 0.00509	0.97774 ± 0.00644	0.80769 ± 0.04420	0.85337 ± 0.08728	0.77059 ± 0.03222	0.98925 ± 0.00286	0.06321 ± 0.00623	0.79830 ± 0.04825	343,201 ± 0.00000	267,115,168 ± 0.00000	94.17146 ± 1.66290
BarGraph	0.99220 ± 0.00274	0.98057 ± 0.00279	0.96961 ± 0.00591	0.74205 ± 0.03391	0.76914 ± 0.07782	0.72353 ± 0.04922	0.98821 ± 0.00298	0.08000 ± 0.02357	0.72835 ± 0.03530	2,795,681 ± 0.00000	905,671,808 ± 0.00000	76.31179 ± 0.78716
Combination	0.99204 ± 0.00303	0.97986 ± 0.00594	0.96608 ± 0.00550	0.69829 ± 0.04700	0.75119 ± 0.05739	0.65294 ± 0.04362	0.98512 ± 0.00242	0.09018 ± 0.01523	0.68248 ± 0.05021	1,984,057 ± 0.00000	517,548,448 ± 0.00000	73.68748 ± 0.70628
SuperTML	0.98970 ± 0.00590	0.98304 ± 0.00237	0.98516 ± 0.00201	0.87574 ± 0.01504	0.88548 ± 0.05392	0.87059 ± 0.04922	0.98777 ± 0.00760	0.05496 ± 0.01888	0.86914 ± 0.01571	185,833 ± 0.00000	259,563,040 ± 0.00000	133.64104 ± 1.96535
FeatureWrap	0.98280 ± 0.00201	0.96572 ± 0.00366	0.93958 ± 0.01746	0.48403 ± 0.02997	0.54888 ± 0.13640	0.46471 ± 0.08675	0.90951 ± 0.01294	0.16759 ± 0.02673	0.46505 ± 0.03418	380,257 ± 0.00000	11,052,288 ± 0.00000	49.26098 ± 0.49129
BIE	0.98061 ± 0.00868	0.96608 ± 0.00457	0.97774 ± 0.00426	0.81839 ± 0.03416	0.81121 ± 0.07508	0.84118 ± 0.11122	0.99103 ± 0.00178	0.06217 ± 0.00518	0.81103 ± 0.03586	450,257 ± 0.00000	572,690,016 ± 0.00000	99.25059 ± 4.39414

---