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Then, in view of the practical demands on reliability and further improving the prediction accuracy, a bootstrap strategy based on QR-ESN is designed to construct the confidence intervals and the prediction ones via combining with the regression models of various quantiles.
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To account for the potential bias due to the use of the same dataset for both model construction and model evaluation, we deployed a contextually novel strategy, based on the 0.632+ bootstrap approach [42], [43], to support the statistical validity of the relative accuracy of the classifiers reported in this paper.
Unless otherwise stated, the bootstrap results are based on 1000 bootstrap samples.
Bootstrap support was based on 100 bootstrap replicates.
The bootstrap estimate was based on 5000 bootstrap samples.
For kappa, we provide bootstrap confidence intervals based on 1,000 bootstrap replications.
Bootstrap confidence intervals based on 1000 bootstrap samples were also estimated for all AUCs.
Unless otherwise stated, the bootstrap results are based on 10,000 bootstrap samples.
Due to small sample size, 95%% bootstrap confidence intervals based on 10,000 bootstrap samples were obtained.
Bootstrap values based on 1000 replicates were indicated above nodes.
Bootstrap values (based on 1,000 replicates) are indicated at the tree nodes.
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