Sentence examples for predictions to classify from inspiring English sources

Specifically, we train ensembles of Naïve Bayes classifiers using resampled balanced subsets and use their averaged predictions to classify the test instances.

These parameters are used to build prediction models to classify a movie in one of nine success categories, from a "flop" to a "blockbuster".

CPSC and PPAC predictions were combined to classify each mutation as 'high risk' or 'low risk' of association with ccRCC.

Class prediction was assessed to classify and to predict in which class was enclosed a sample in relation to its expression profile, using multivariate classification methods showed in Supplementary Table S4.

The prediction rule used to classify must be the rule defined in the initial study and it should be described in the protocol of the validation study.

The cutoff points of total scores, determined by statistical significance and the yield of prediction, were used to classify HIV patients into the low- or high-risk groups.

A receiver operating characteristic (ROC) curve that depicted the relationship between the proportion of true positives and the proportion of false positives was drawn, depending on the prediction rule used to classify the patients as having cardiac dysfunction.

To determine how well FeUrea distinguished transient from persistent AKI (our primary objective), we plotted the receiver-operating characteristic (ROC) curves of the proportion of true positives against the proportion of false positives, depending on the prediction rule used to classify patients as having persistent AKI.

To assess the performance of urinary indices or their changes over the first 24 hours in distinguishing transient AKI from persistent AKI, we plotted the receiver-operating characteristic curves for the proportion of true positives against the proportion of false positives, depending on the prediction rule used to classify patients as having persistent AKI.

Prediction results for these two avian and human strains demonstrate the high accuracy of the host tropism prediction system, where despite making each protein prediction independently and not being influenced by other predictions, it is able to classify all proteins in each strain correctly.

After selection of predictions according to their sequence and structural features, an effort was made to classify predictions and to validate FindMiRNA combined with the adopted parameters and thresholds as effective instruments for de novo identification.