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Feature selection schemes were compared by averaging cross validation error rates (CVER), sizes of feature sets found and searching times across the classification models (Table S2).
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The algorithms' performance is subsequently evaluated and compared by averaged confusion matrices and ultimately by boxplots of misclassification rates.
Groups then compared by their average group payoffs according to the above described competition rules.
Finally, the performance of all models is compared by three criteria (average profit per day, car utilization ratio and reservation acceptance ratio) for the aforementioned service models.
Different test and reference treatments were compared by applying the average bioequivalence method.
Methylated and unmethylated promoter CGIs were compared by sequence for average length, GC content, and ObsCpG/ExpCpG ratio.
The real-time value for each sample was averaged and compared by using the CT method.
The effectiveness of the proposed layout at the study location for varying flow level is evaluated by comparing average delay, average stop delay, average number of stops per vehicle, average queue length, and maximum queue length.
Our data supports an advantage of a multi-subject joint image reconstruction compared to individual reconstructions followed by averaging.
The averages were compared by the least significant difference test at 5% of probability.
These averages were compared by non-parametric Kruskal Wallis test followed by Dunn multiple comparison test.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com