"model prediction accuracy" is a correct and usable phrase in written English.
You can use it when referring to a model's ability to accurately predict something or quantify its accuracy in predictions. For example: "The model prediction accuracy of our weather forecasting system was very impressive."
In order to enhance the model prediction accuracy and generalization capability, a hybrid modeling approach for cobalt oxalate synthesis process in cobalt hydrometallurgy is developed by combining simplified first principle model with PLS model.
The model prediction accuracy relative to FEM simulations is found to be satisfactory, with errors of less than 10%.
Further, the criterion for screening candidate models must be based on estimation of ˆμ and ˆVar rather than the model prediction accuracy or goodness of fit.
RBDFs were not developed for these cases; however, either specific ways to improve the model prediction accuracy were recommended or improved models were provided and demonstrated.
The effects of manufacturing errors on the model accuracy are analyzed, with the results indicating that the first mode is greatly affected by the manufacturing errors, while the thickness variation of the cartwheel flexure hinge has the largest effect on the model prediction accuracy.
The model prediction accuracy may vary at different development stages.
This technique has been shown to improve on GM 1,1) and grey Markov model prediction accuracies.
From Table 1, the two models' prediction accuracy in describing solute transport can be compared.
We used 10-fold CV (Hastie et al. 2001) to assess the models' prediction accuracy, to select the number of PLS components to use in the final prediction models, and to compare predictions generated using PLS only to our primary models, which used both PLS and universal kriging.
However, as for pedigree-based models, prediction accuracy of marker-based models decreased and fell near zero when close (CV2, Table 5) and all known relatedness (BG1 → BG2, Tables 4 and 5) was removed between the training and testing sets, respectively, similarly to what was observed in another GS study on a white spruce population of larger effective size [ 18].
Overall, the DF model increases prediction accuracy by about 5% compared with the DT model, from 89.4 to 94.7%.
