Sentence examples for model validation metrics from inspiring English sources

In this paper, by extending the concept of "area metric" and the "u-pooling method" developed for validating a single response, we propose new model validation metrics for validating correlated multiple responses using the multivariate probability integral transformation (PIT).

Results of the resource selection models, including their validation metrics, indicate that the relative displacement index assigned data to the respective behavioral modes in a biologically meaningful way.

The model was validated extensively using different validation metrics to check the robustness and predictivity of the model for regulatory acceptance and enhancing confidence in QSPR predictions.

We validate the BASEMENT simulation model with insurance claims using conventional validation metrics.

The proposed approach is compared to standalone ANN models and validated using well-known statistical validation metrics including coefficient of determination (R2), root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and t-statistics.

The applicability of these two models is evaluated by the validation metrics.

State-of-the-art validation metrics for numerical models were used to quantify the agreement between the data sets.

The agreement between measured data and the predictive capabilities of sixteen models were quantitatively assessed using two validation metrics.

Overall, the model in V 2, ρ, D, and A recorded the best performance metrics amongst the calibration dataset, and relatively good metrics when validated; however, the simpler model in V 2 and ρ recorded better validation metrics.

Table 2 Internal and external validation metrics for the PCM models   Adenosine Receptors Dataset     R int 2 RMSEPint R 0 ext 2 RMSEPext GP Bessel 0.64 0.70 0.70 0.63 GP Laplacian 0.67 0.68 0.67 0.66 GP Norm.

Generating and evaluating atomic models based on such maps would be greatly facilitated by independent validation metrics for assessing the fit of the models to the data.