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Rather than assessing prediction just for a single threshold, the performance over a range of cutoffs, including area under the ROC curve or area under the precision-recall curve metrics, is commonly reported.
Instead of generalized pareto distribution (GPD) and exponential distribution (ED) models popularly applied to predict the probability of the exceedances of peak over threshold, the performance of the general logistic distribution (GLD) models is analyzed.
Over that threshold, the performance drops to a stable value around 9.5 Mbps.
Another observation is the fact that when the ASR is below a certain threshold, the performance stays within a reasonable range and the throughput does not drop significantly.
With the same threshold the performance on LUAD samples was the poorest, reaching 77% overall accuracy (correlation 0.55 and AUC 0.83) using an average number of 274 positively and 28 negatively scored genes.
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Based on the threshold, the performances of RMN algorithm were more than 0.82 (see Table 1).
Cell edge is identified using a pilot-channel SINR threshold, and the performance results depend on the threshold value.
In this study, we have conducted a threshold analysis for varying methylation levels to identify whether setting a methylation level threshold increases the performance of functional enrichment.
In addition, we study how the cell-edge SINR threshold influences the performance.
It is obvious that the choice of the threshold controls the performance and the efficiency of the ED[21].
We first show that as the SNR is increased beyond a value, referred as threshold SNR, the performance degrades.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com