Table 19: Dataset metafeatures that are most correlated with the difference in normalized log loss between pairs of algorithms (the loss of Alg. 1 minus Alg. 2). Correlations are taken over all 10 splits of all 133 datasets in which CatBoost, XGBoost, ResNet, and SAINT ran successfully. The 10 dataset attributes with the largest absolute correlation are listed for each pair of algorithms. Attribute names correspond to the naming convention used by PyMFE.
Alg. 1	Alg. 2	Corr.	Attribute Name
CatBoost	ResNet	-0.25	Maximum skewness of all features.
CatBoost	ResNet	-0.24	Range of the skewness of all features.
CatBoost	ResNet	-0.23	Log of the standard deviation of the kurtosis of all features.
CatBoost	ResNet	-0.23	Log of the standard deviation of the skewness of all features.
CatBoost	ResNet	0.22	Log of the median of the absolute value of the covariance between all feature pairs.
CatBoost	ResNet	0.21	Log of the median of the standard deviation of all features.
CatBoost	ResNet	0.21	Log of the median of the variance of all features.
CatBoost	ResNet	0.20	Log of the median of the maximum value of all features.
CatBoost	ResNet	0.20	Best performance of a naive Bayes classifier trained over 10-fold CV.
CatBoost	SAINT	0.26	Best performance over all 10 folds, of 10-fold CV of a single-node decision tree fit using the least-informative feature.
CatBoost	SAINT	0.25	Average performance over 10-fold CV of a single-node decision tree fit using the least-informative feature.
CatBoost	SAINT	0.24	Median performance over 10 folds, for 10-fold CV of a single-node decision tree fit using the least-informative feature.
CatBoost	SAINT	-0.24	Log of the worst performance over 10-fold CV of a single-node decision tree fit using the most-informative feature.
CatBoost	SAINT	-0.23	Log of the kurtosis of the performance of a single-node decision tree fit using the most-informative feature, over 10-fold CV.
CatBoost	SAINT	0.23	Log of the Shannon entropy of the target.
CatBoost	SAINT	-0.23	Log of the best performance of elite-nearest-neighbor over 10-fold CV.
XGBoost	ResNet	-0.29	Maximum skewness of all features.
XGBoost	ResNet	-0.28	Range of the skewness of all features.
XGBoost	ResNet	-0.28	Log of the standard deviation of the kurtosis of all features.
XGBoost	ResNet	-0.27	Log of the standard deviation of the skewness of all features
XGBoost	ResNet	0.25	Log of the median value of the absolute covariance between all pairs of features.
XGBoost	ResNet	0.24	Log of the median standard deviation of all features.
XGBoost	ResNet	0.23	Log of the median variance of all features.
XGBoost	ResNet	0.23	Log of the median maximum-value of all features.
XGBoost	ResNet	0.22	Best performance of a naive Bayes classifier over 10-fold CV.
XGBoost	ResNet	0.22	Log of the best performance of a naive Bayes classifier over 10-fold CV.
XGBoost	SAINT	-0.23	Noisiness of the features: (ΣiSi - ΣiMI(i,y)) / ΣiMI(i,y), where Si is the entropy of feature i, and MI(i,y) is the mutual information between feature i and the target y.
XGBoost	SAINT	-0.22	Log of the standard deviation of the kurtosis of all features.
XGBoost	SAINT	-0.20	Log of the standard deviation of the skewness of all features.
XGBoost	SAINT	-0.20	Best performance over 10-fold CV of a single-node decision tree fit using the most-informative feature.
XGBoost	SAINT	-0.20	Maximum skewness of all features.
XGBoost	SAINT	0.19	Log of the Shannon entropy of the target.
XGBoost	SAINT	-0.19	Log of the standard deviation of the absolute correlation between all pairs of features.
XGBoost	SAINT	-0.18	Range of the skewness of all features.
XGBoost	SAINT	0.18	Log of the range of the performance of a decision tree trained on a random attribute, over 10-fold CV.