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Therefore, to improve the model performance an adequate identification of feasible products sequences is required.
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To improve the model performance, a locally tuned MeSAA was developed through the use of a locally tuned ΔpCO2@bio term.
To assess the impact of parameterization choice on model performance, a number of commonly used formulations for aerodynamic and surface resistances were substituted into the different formulations.
In order to optimize the model performance, a recently developed optimization technique based on rate rules (Cai and Pitsch, 2014) is extended in this study.
In order to improve the model performance, a type of enhanced cat swarm optimisation (ECSO) is adopted to optimise the key parameters of SVM.
To evaluate pragmatic model performance, a multiple imputation approach which excludes the observed outcome could be applied to the validation dataset (approach V4 in Table 7).
For measures of pragmatic model performance, a strategy for handling missing predictors in the future clinical setting must be chosen and is evaluated as part of the risk prediction algorithm.
In order to evaluate the models performance, a receiver operating characteristic (ROC) curve was built and the area under ROC curve (AUC or C statistics) was used to measure the discriminative power.
The AUC is a threshold independent measure of model performance, where an AUC value of 1 indicates optimal performance, and AUC = 0.5 indicates a model performing no better than a randomly generated one.
When reach-level habitat variables were added to the models there was a consistent improvement in model performance and an increase in the number of significant models.
The AUC statistic is a threshold-independent measure of model performance; theoretically, an AUC score of 1.0 indicates optimal performance, while AUC = 0.5 indicates a model that predicted occurrences no better than at random [40].
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com