Classification statistics

	framework	AutoGluon	FEDOT	H2O	LAMA
Dataset name	Metric name
APSFailure	auc	0.990	0.991	0.992	0.992
Amazon_employee_access	auc	0.857	0.865	0.873	0.879
Australian	auc	0.940	0.939	0.938	0.945
Covertype	neg_logloss	-0.071	-0.117	-0.265	nan
Fashion-MNIST	neg_logloss	-0.329	-0.373	-0.380	-0.248
Jannis	neg_logloss	-0.728	-0.737	-0.691	-0.664
KDDCup09_appetency	auc	0.804	0.822	0.829	0.850
MiniBooNE	auc	0.982	0.981	nan	0.988
Shuttle	neg_logloss	-0.001	-0.001	-0.000	-0.001
Volkert	neg_logloss	-0.917	-1.097	-0.976	-0.806
adult	auc	0.910	0.925	0.931	0.932
bank-marketing	auc	0.931	0.935	0.939	0.940
blood-transfusion	auc	0.690	0.759	0.754	0.750
car	neg_logloss	-0.117	-0.011	-0.003	-0.002
christine	auc	0.804	0.812	0.815	0.830
cnae-9	neg_logloss	-0.332	-0.211	-0.262	-0.156
connect-4	neg_logloss	-0.502	-0.456	-0.338	-0.337
credit-g	auc	0.795	0.778	0.798	0.796
dilbert	neg_logloss	-0.148	-0.159	-0.103	-0.033
fabert	neg_logloss	-0.788	-0.895	-0.792	-0.766
guillermo	auc	0.900	0.891	nan	0.926
jasmine	auc	0.883	0.888	0.888	0.880
jungle chess	neg_logloss	-0.431	-0.193	-0.240	-0.149
kc1	auc	0.822	0.843	nan	0.831
kr-vs-kp	auc	0.999	1.000	1.000	1.000
mfeat-factors	neg_logloss	-0.161	-0.094	-0.093	-0.082
nomao	auc	0.995	0.994	0.996	0.997
numerai28_6	auc	0.517	0.529	0.531	0.531
phoneme	auc	0.965	0.965	0.968	0.965
segment	neg_logloss	-0.094	-0.062	-0.060	-0.061
sylvine	auc	0.985	0.988	0.989	0.988
vehicle	neg_logloss	-0.515	-0.354	-0.331	-0.404