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Cross-validation is a technique to assess how accurately a predictive model will perform on an independent data set and whether the model recognizes a pattern that is generalized enough to apply to unseen data [ 74, 75].
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Such ratings are rarely useful and general enough to apply to a diverse population.
Our method is generic enough to apply to many areas of computational modelling.
To conclude, it is enough to apply Mazur's theorem.
Clearly some of these moral frameworks are comprehensive enough to apply to non-voluntary contexts.
In addition, the selection method should be easy enough to apply in field conditions.
Therefore, we assumed that effective population sizes are large enough to apply these criteria.
The methodology we describe is general enough to be applied to any other type of cancer.
These models are general enough to be applied to any VANET application.
The proposed approach is general enough to be successfully applied to other cases of interest.
Therefore, future studies should look for a standard and generalized procedure to apply and calculate LyE.
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