Sentence examples for assessed prediction from inspiring English sources

Crossa et al. (2010) assessed prediction problems relevant to plant breeders: (1) predicting the performance of genotypes that have not been evaluated in any environment (assessed by cross-validation CV1), and (2) predicting the performance of genotypes that have been evaluated in some environments, but not in others (assessed by cross-validation CV2).

The strengths and weaknesses of all the assessed prediction methods are discussed.

We assessed prediction performance using the Matthews Correlation Coefficient for binary data (MCC, eq. 4).

We assessed prediction performance by calculating the area under the Receiver Operating Characteristic curve (AUC) for predictions on separate test set data.

Subsequently, we assessed prediction accuracy using training-testing (i.e., TRN-TST) random partitions (see below).

We assessed prediction performance on the 120 training series as described above and observed that DiriSim clearly outperforms both extrapolation methods (Fig. 3c).

Rather than assessing prediction just for a single threshold, the performance over a range of cutoffs, including area under the ROC curve or area under the precision-recall curve metrics, is commonly reported.

The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy.

The number of source parameters that we used to assess prediction uncertainty was more than 12.

This simultaneous calculation is a big advantage compared to other approaches, which assess prediction reliability and usually need additional iterations.

As a general rule, external validation tests are considered the true standard to assess prediction in QSAR modeling.