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The classifier is trained for 100 rounds of boosting under 10-fold stratified cross validation (CV) and achieves a mean test split AUROC of 0.96.
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All algorithms use the same training and test splits, which means that the performance values of two algorithms on a data set split can be paired.
In Table 2 we report for each ncRNA class the average AUC and the overall weighted mean on all classes averaged over 10 random test splits of Development Data.
The test splits file ws97-test-convs.list used in Stolcke et al. (2000).
The 1855 instances are split into training and test splits for 10 stratified folds.
You can make it mean tested.
Assessing doesn't mean testing.
Undergo a "means test.
Means-test Social Security.
That's what means-testing means.
Should we means-test it?
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