Sentence examples for model performance based from inspiring English sources

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.

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.

As noted, we compared four separate models' performances based on different data: L-PLS and K-PLS Polynomial Kernel (KPLS-Poly) based on the Coarse Feature Reduced (CFR) data, and on the Fine Feature Selected FFSS) data respectively (the FFS-data is actually processed by both CFR and FFS).

This paper discusses an integrated fire-structural simulation model for performance based design.

A large scale centrifuge test of a geomembrane-lined landfill subject to waste settlement and seismic loading was conducted to help validate a numerical model for performance based design of geomembrane liner systems.

To evaluate the proposed models, speaker identification performance based on such models is compared separately with that based on each of LTRSPHMM1s, LTRSPHMM2s, and CSPHMM1s in the two talking environments.