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