The evaluation of the model performance, based on the test dataset, showed a root mean square prediction error, expressed relative to the observed mean, of 2.65%, 7.67% and 7.61% of the observed mean for acetate, propionate and butyrate, respectively.
An evaluation of the model performance based on statistical rules yielded time-averaged Coefficient of Determination, Root Mean Squared Error and the Mean Absolute Error ranging from 0.9945 0.9990, 0.0466 0.1117, and 0.0013 0.0130, respectively for individual stations.
We evaluate the model performance based on the mean classification accuracy of 100 independent 10-fold cross-validations.
This comparative analysis provides a measure of model performance based on the ability of the CA-BCM to accurately estimate basin discharge.
We evaluated model performance based on root mean square error (RMSE) and RMSE as a percent of the mean of the field inventory TLB (%RMSE), and absolute bias (Eqs. 5, 6, 7) and residual diagnostics.
While some portion of trait variation in our models is likely to be phylogenetically structured, model performance, based on cross-validation, was only modestly improved by including phylogenetic measures, and trait relationships were not altered.
We propose a further strategy to evaluate pragmatic model performance based on a set of partial prediction models.
Model performance based on P7 will be poorer than that from P8 or P9 because of the additional between-imputation variability induced by the second imputation procedure.
Caution is needed when making conclusions about model performance based on p-values, such as from the Mann–Whitney U test.
Biochemical markers to improve model performance based on noninvasively measured risk factors could be particularly useful if diabetes risk screening involves a multistep procedure, with simple questionnaires or noninvasive information at the start and more costly measurement of biochemical indicators in prescreened individuals during a second step.
To study the benefit of data integration based on poe compared to that based on the linearly rescaled values, we compared the model performances based on data integration by these two methods.
