Unverified50% confidenceBenchmarkExact time
Assigning different ranks to experts causes no loss for average merging and utility-based selection methods, and instead brings up to 1.8 percentage points of improvement in multi-domain accuracy.
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50%
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Long-term
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11/5/2026
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UnverifiedSelecting components by loss effect rather than by size preserves all three experts and keeps accuracy nearly flat across the 8x rank range (fluctuating by 0.7 percentage points).79% similarUnverifiedMacro-average accuracy first computes the accuracy of each class separately and then takes the arithmetic mean, making it more sensitive to the performance of minority classes; overall accuracy is the proportion of all samples predicted correctly and is easily affected by the class sample size distribution.77% similarUnverifiedIn comparisons with optimizers using the same criterion, the hybrid method won 6, tied 41 and lost 19 of 66 Holm-corrected accuracy comparisons.71% similarUnverifiedPrediction accuracy depends more on the size of the reference corpus than on how correlation is modeled.71% similarUnverifiedA study decomposing ensemble gains points out that reported "ensemble gain" numbers can flip sign depending on the choice of baseline (+9.67pp vs -8.33pp on different datasets), and that many so-called ensemble gains are merely artifacts of comparison choices.71% similar
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