Sentence examples for accuracy of predictors from inspiring English sources

The predictive accuracy of predictors generally ranges from 65% to 100% (mean, 82%) [58].

Combining the relative efficiency with the mapping accuracy of predictors could also be helpful to gain a better understanding of the underlying mechanisms in an association study.

ROC (receiver-operating characteristic) curves were generated and the AUC (area under the curve) was calculated to assess the accuracy of predictors [ 24].

Our method can also be applied in elucidating the maximum accuracy of predictors that integrate features such as gene expression, de novo mutation, body mass index, and lifestyle (which are not fully inherited).

Previous work by Pryce et al. [ 13] using the Holstein, Jersey, and Fleckvieh breeds showed that combining divergent subpopulations in the training set does not improve the accuracy of genomic predictors over within-breed derived predictors.

Work in other species [ 14] has shown that population structure can account for a substantial portion of the accuracy of genomic predictors but accounting for this structure can decrease the reliability of across-breed genomic predictors.

The prediction accuracy, sensitivity and specificity were calculated for each of the predictors from each array platform and are displayed in both figure 1 and table 2. The accuracy of our predictors ranged from 86%100%%, with a mean accuracy of 95%.

A practical implication of this is that we can improve the accuracy of genetic predictors without the need to access large-scale individual-level data sets, and we can potentially combine multiple predictors based on different summary statistics, e.g. combine risk scores based on multiple GWAS studies if a GWAMA is not available.

On the negative side, the accuracy of HB predictors is highly path-dependent.

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]).

In summary, there is no shortage of observational investigations examining the accuracy of weaning predictors.