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The best predictive model was applied to the airborne AISA-EAGLE hyperspectral data to model the phenological macro-stages of barley at the landscape level.
The best predictive model was selected after conducting a sensitivity analysis for determining the optimum GA parameters coupled with a trial-and-error method for finding the optimum network architecture i.e. number of hidden nodes.
The best predictive model was selected using R library 'glmulti' [ 17].
Within the 2−ΔΔCT data set, the best predictive model was a combined gene expression model of ANPEP and ABL1.
In each model, the multicollinearity was investigated between each variable: the variable that led to the best predictive model was retained.
However, the time difference (i.e., latency) for the best predictive model was only 22.5 years (from mid-1977 to 2000), and thus the observed relationship may have reflected only early effects.
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The identification of the best predictive models is only the first step when applying risk adjustment procedures; variables in predictive models might be homogeneously distributed across hospitals and therefore are not actual confounders even if strongly associated with the outcome.
27 Once the 'best' predictive model was identified for each of the two outcomes, the variable 'type of cholecystectomy' was included, and the adjusted OR of LC versus open surgery was estimated, with a corresponding 95% CI and p value.
To do a certain task, the best available predictive model is selected.
The total number of peaks yielded the best predictive model and is used for optimization of parameters setting.
For all three sub-samples, the performance of the predictive models was similar to the whole sample: the models that were adjusted for prior cost performed the best.
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