In this study, through shape error metric (" Validation metrics" section), cMEM reconstructed time courses were evaluated for the first time.
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).
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.
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.
We validate the BASEMENT simulation model with insurance claims using conventional validation metrics.
Using three different validation metrics, namely, mutual information, channel diversity, and the novel Environment Characterisation Metric, we find that the RCM is able to reflect the measured environment remarkably well.
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.
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.
In this work, simple validation metrics applicable to testing both model accuracy and consistency with experimental data are developed.
The agreement between measured data and the predictive capabilities of sixteen models were quantitatively assessed using two validation metrics.
