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To examine both the impacts of bias and scale in accuracy assessment, we assessed the predictive accuracy of GNN maps in western and southern Oregon.
Chu et al 30 assessed the predictive value of different task combinations for predicting falls.
We assessed the predictive performance of the scoring algorithm for predicting cefepime-resistance and found satisfying performance (median value of AUROC of 0.76; CI 95%, 0.67 0.84).
We assessed the predictive ability of the models using the Pearson correlation coefficient between the predicted GEBV and the observed phenotypic value.
Second, we have assessed the predictive power of several sequence based features, and achieved superior prediction accuracy through integration with a probabilistic framework and intelligent feature selection.
We directly assessed the predictive performance of the regression using leave-one-out (LOO) error estimation, predicting the PMI of each sample using a model trained on the remaining samples.
In this study, we assessed the predictive capability of these methods with respect to simulated groundwater residence times.
This study assessed the predictive validity of this profile in older persons seeking care after a fall.
We assessed the predictive value of adiponectin for atherosclerosis related cardiovascular events in type 2 diabetic dialysis patients.
We assessed the predictive power of these models using the area under the receiver operating characteristic (AUC).
The first one assessed the predictive skill of the models through area under the curve (hereafter, AUC; Phillips et al. 2006).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com