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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.
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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.
Therefore, to improve the model performance an adequate identification of feasible products sequences is required.
The models performance had a mean crossvalidation r2 of 0.13.
By limiting our analysis to only patients referred to spirometry we provide a conservative estimate of the models performance if applied to a general population.
Therefore, it can be concluded that to evaluate a models' performance, it is necessary to consider these statistics together.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com