Sentence examples for optimal prediction accuracy from inspiring English sources

With simple CV, this default option led to the optimal prediction accuracy under each setting, for instance, in the binary IC data set.

Gorjanc et al. [ 10] reached a similar conclusion for GS studies, finding that optimal prediction accuracy was obtained with low depth (around 1 2) sequencing of many individuals.

Sparse learning is an area of machine learning research which can be used to find a small number of important predictors to achieve optimal prediction accuracy.

The number of alpha values used is a function of the extent of difference between immunological statuses: the lower the difference, the more alphas will be needed by the SVM (or any other feature selection algorithm) to reach optimal prediction accuracy.

Class prediction was performed by using prediction analysis of microarrays (PAM), a statistical package [ 45] that applies nearest shrunken centroid analysis and cross-validation to determine a minimal set of predictor genes that achieve optimal prediction accuracy for sample classification [ 46].

This further underlined the advantage of using Diversity and Evenness profiles in a prospective immunodiagnostics pipeline (Fig.  1) for a more accurate and robust capture of the immunological status of immune repertoires.> -wrap-foot> The median number of alpha-values employed to reach optimal prediction accuracy (BACC) ranged between 2 and 11.

Indeed, it has been shown that single-locus p-value-based selection strategies for constructing prediction models may lead to sub-optimal prediction accuracies [ 17].

Figure 6 shows, for each classification experiment and feature type, the optimal SVM prediction accuracy over all subsets.

SVM feature selection determined a median number of 2 to 11 alpha values as optimal for reaching the highest prediction accuracy (Table  1).

In this case, we use the results from the 'outer' 10-fold cross-validation to select the optimal model and then use the full training population data set to estimate the parameters of this optimal model and compute the prediction accuracy on the testing data (Supplementary Material, Fig. S5).

Then, the prediction accuracy and the optimal separation robustness, including the uncertainty study, were evaluated.