Sentence examples for maximum predictive ability from inspiring English sources

An additional RR BLUP was performed using as covariates only the marker subset for which maximum predictive ability was obtained.

At the point of maximum predictive ability, it was 1.33, and it kept decreasing even if insignificant markers were added.

In these cases, maximum predictive ability was reached with smaller marker subsets (110 590 markers) and decreased with the addition of more markers.

The number of ICA brain networks included in the classifier was restricted so that maximum predictive ability was obtained (increasing from 39 to 70 independent components decreased sensitivity to 47.3%, SE = 7.27).

However, for traits where the maximum predictive ability (Density, Rust_bin, Rust_gall_vol) was reached with a smaller number of marker (<600), RR BLUP B performed significantly better than RR BLUP.

The minimum number of clusters in HC-PLSR giving (approximately) maximum obtained predictive ability (maximal R2 and minimal RMSEP) was chosen for each state variable time series.

The PLS model built from this dataset has 4-components with both R2 (goodness of fit, maximum 1) and Q2 (predictive ability, maximum 1) values more than 0.9, where 1st and 2nd components account for about 80 and 6% of the variations respectively.

Model evaluation was based on threshold-independent measures of performance, area under the Receiver Operating Characteristic curve (AUC) statistics; scores closer to 1 (maximum) indicate higher predictive ability and AUC > 0.5 indicates better-than-random model prediction [ 56].

The predictive ability of maximum weighted dose and the maximum 6-h mean dose was similar [respectively, 0.73 (0.63–0.83) p < 0.001 and AUROC = 0.73 (0.62–0.83), p < 0.001] although inferior to that of the WMD.

The maximum relative error in prediction (5.26) showed that the predictive ability of the model was satisfactory and it can be used for designing similar reactants with efficient sulfonation reaction.

While the goodness of fit increases by each extracted PLS component, the predictive ability typically reaches a maximum and then declines when the model becomes too complex.