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The comparison shows that the proposed combination of equivalent age and the new formula can reduce the maximum relative error substantially from 73%to15%5% than those algorithms which do not consider equivalent age or our proposed formula.
Hence HQSAR performance followed the trend seen for CoMFA: cross-validation under-estimated the predictive error substantially for the biased subset (i.e., was overly optimistic about the extensibility of the model) and over-estimated the predictive error slightly for the boosted training sets.
The combination of the six features from all three domains improves the error substantially compared to even the best single feature.
Unlike the five most important predictors, the nested risk assessment models could not reduce the OOB classification error substantially below 0.4.
With a percentage error substantially below 30%, as demonstrated in this study, interchangeability can be safely assumed despite these methodological issues.
In general with unequal-sized trials, the type I error of DL was substantially inflated even with minimal heterogeneity, while with equal-sized trials minimal or modest heterogeneity did not inflate the type I error substantially.
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They cannot be directly controlled, and their forecast errors substantially exceed the forecast error of load.
Subjects in both groups reduced their spatial errors substantially from early to late trials on day1 (Fig. 4A compare red traces, FFv: F 3,5) = 9.6, p<0.00001, FFnv: F 3,5) = 20.3, p<0.00001, post-hoc p<0.001).
Thus, mass errors substantially higher than 5 ppm are possible.
These errors substantially increase as the material being analyzed increases in shape and size heterogeneity.
Gene-calling errors substantially degrade the integrity of comparative genomics, which increasingly serves as a foundation for biology and medicine.
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CEO of Professional Science Editing for Scientists @ prosciediting.com