Similar conclusions can be drawn from simulation results for CR systems with unequal setting parameters, which are not provided due to the limited space.
"There's a systematically unequal set of demands on those schools".
Formally, consensus methods apply when all trees have exactly the same set of taxa, while supertree methods extend consensus methods to situations with unequal taxon sets [ 25].
According to the results, the unequal-training set achieved better performance than the equal-training set because it has similar distributions to the testing set.
Based on the results, the MAE of the ground truth age groups less than 35 years old from the unequal-training set are smaller than that from the equal-training set, whereas the MAE of age groups larger than 40 years old from the unequal-training set are larger than that from the equal-training set, which can be understood according to the different distributions of training subjects.
The second training subset, referred to as the unequal-training set, is considered to have a similar distribution as the testing set in terms of gender and age group and consists of subjects randomly chosen from the entire training set, with the same size as the equal-training set.
This is because the unequal-training set is assumed to contain more appearance variations (e.g., variations in clothes and hair style) than the equal-training set considering the number of females, which affects the training of the regression model.
By contrast, the age groups less than 5 years old in the unequal-training set have larger MSEs than the other age groups, which also indicates that there is room to improve performance.
The data set is randomly partitioned into two unequal subsets, the learning set (also called the training set) and the test set.
To address a potential bias of this unequal distribution the set of n = 788 miRNA was split into two separate subsets A and B. Set A contains 23% (n = 181) miRNA which interact with 75% of the n = 2954 genes.
