Sentence examples for multiple accuracy metrics from inspiring English sources

The models were then judged in terms of multiple accuracy metrics applied across two datasets: (1) the calibration dataset from which the models were derived, and (2) an external validation dataset not used in model development.

Accuracy of models were compared using multiple accuracy metrics (R 2 marg, R 2 cond, AIC, RMSE, MAPE, and TP rate), to avoid incorrect interpretation which could result from a single metric.

For accuracy metrics, we used the mean RMSE.

See see Additional file 1 for definitions of accuracy metrics.

Accuracy metrics for the latter dataset were of particular interest.

They are evaluated using three accuracy metrics, Kappa index, classification accuracy and classification error.

Slightly improved accuracy metrics were obtained with the three more complex models (E + T, E + D + H, V 2 + ρ + T) all with similar calibration accuracy metrics.

The SVM and RF are evaluated using Kappa index, classification accuracy and classification error as accuracy metrics.

The accuracy metrics used for comparing results are the Kappa index, the classification accuracy and the classification error.

Variance is critical information when considering map accuracy, yet commonly reported accuracy metrics often do not provide that information.

All accuracy metrics for the current predictions are summarized in Table 1.