Sentence examples for both classifiers was evaluated from inspiring English sources

Prognostic performance of both classifiers was evaluated in three ways: Kaplan-Meier survival curves, stage-adjusted Cox proportional hazard ratio modeling followed by the Wald test (R package: survival v2.35-8 v2.35-8nand classification measures.

Both classifiers were evaluated via the same 5-fold cross-validation procedure as BPB-PPMS.

Both classifiers were evaluated for the task of assigning GO terms to a just published abstract.

To assess the influence of different pre-processing schedules on signature performance, the Director's Challenge datasets were pre-processed in 24 different ways and both classifiers were evaluated in each of these datasets.

The performance of the classifiers was evaluated by tenfold cross-validation.

The spatial and temporal transferability of the classifiers was evaluated using data sets from four different agricultural landscapes with different spatial extents and from different years.

The performance of the classifiers was evaluated with leave-one-out cross validation (Table 5).

The performance of grammar classifiers was evaluated using the Leave‐One‐Out and 4‐fold Cross‐Validation schemes (LOOCV and 4‐fold CV, respectively) and on an independent test set.

The performance of these classifiers was evaluated, based on the log-rank test, on the time-to-event data on the Chin Loi training set.

The classifiers were evaluated both in an in-domain setting, with a standard leave-one-out cross validation procedure and in a cross-domain setting, where they were trained on one domain and tested on the other (Danescu-Niculescu-Mizil et al. 2013), achieving accuracy of 78.19% and 75.43%, respectively.

The classifiers were evaluated on both the NSI/SI and R5/X4 datasets using ten times ten-fold (10×10) cross-validation.