Sentence examples for multiple candidate models from inspiring English sources

Our supervised classification procedure provides multiple candidate models (e.g., 10 in a 10-fold cross-validation).

In cases where multiple candidate models had Δ AICc of ≤2, the model with the fewest variables was selected as the most parsimonious model.

However, most of the time multiple candidate models with different structures can show very similar goodness of fit and also prediction in another experimental condition.

Since the covariate selection process involved fitting multiple candidate models to the same data, set 10 was reserved (i.e., not used for model fitting) to assess whether the covariate selection process contributed to over-fitting.

Because the covariate selection process involved fitting multiple candidate models to the same data, we used set 10 to assess whether the covariate selection process contributed to overfitting, as reported above.

In the case of multiple candidate process models, model discrimination is then performed to choose the best representative one by comparing posterior probability shares.

We then combined forecasts from multiple candidate DWT SVR models using a model averaging technique based upon Akaike's information criterion (AIC).

When multiple candidate logic models appeared to have optimal results within a single training run, the model using the fewest variables was selected, given the preference for parsimonious diagnostic algorithms for potential use by clinicians in low-resource settings.

As an alternative to stepwise procedures, which would have introduced unmanageable noise into our analyses by introducing model selection uncertainty, we computed AIC for each of 726 multiple logistic regression candidate models.

In the case of SNPs with multiple candidate best genetic models, the one with the smallest p value derived from the univariate Cox regression analysis was deemed to be the best genetic model [ 13].

Of those 13 articles developing a multivariable model with multiple candidate predictors for inclusion, the most common approach was to use p-values to decide which factors were included.