The mean squared error, MSE, between model predicted probability and observed binary outcomes is often taken as a single global index of model prediction performance and is known as accuracy.
No difference in model prediction performance was observed for water run-off, reflecting the closed-system nature of this variable.
Similarly, no difference in model prediction performance was found for N2O fluxes, but here the N2O predictions were noticeably poor in both cases.
Results of this kind of study are useful for design, modeling, prediction, performance evaluation and optimization of different kinds of off-road vehicles, earth moving machines, tractors, tillage tools/implements, traction elements, wheeled mobile robots and autonomous traction vehicles.
Comparisons between XBeach results and the Van Der Meer empirical formula for wave transmission behind offshore structures show a very strong agreement, while the real-world scenario indicates promising model prediction performance of dune failure at least one day in advance.
For a particular classifier, various standard performance metrics can be summarized based on the confusion matrix to compare model prediction performance.
Our results demonstrate that there is an interaction between map density and QTL significance threshold on model prediction performance.
Further, the prediction error variance (PEV) V(a - â)) of the sire effects was calculated to provide an informative comparative assessment of model prediction performance.
Although most studies have resorted to using default parameter values, such as regularization constant in the RLS-based models, this approach may lead to biased estimates of the model prediction performance in the test set.
