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Of these three, the jackknife is deem the most objective that can always yield a unique outcome for a given benchmark dataset as elucidated in [29] and demonstrated by Eq.50 of [47].
However, as elucidated by [39] and demonstrated by Eq.50 in [41], among the three cross-validation methods, the jackknife test is deemed the most objective that can always yield a unique result for a given benchmark dataset, and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors (see, e.g. [42] [44]).
However, as elucidated in [18] and demonstrated by Eq.50 of [12], among the three cross-validation methods, the jackknife test is deemed the most objective that can always yield a unique result for a given benchmark dataset and hence has been increasingly and widely adopted to examine the power of various predictors (see, e.g., [42], [46], [51], [53], [55], [65], [66], [67], [68], [69]).
Thus, the outcome obtained by the jackknife test is always unique for a given benchmark dataset [ 26].
Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset.
However, among the three test methods, the jackknife test is deemed the least arbitrary that can always yield a unique result for a given benchmark dataset [ 18].
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CEO of Professional Science Editing for Scientists @ prosciediting.com