Sentence examples for cross validation suggested from inspiring English sources

The cross validation suggested that the estimate of the basic reproduction ratio (R0) was fairly consistent.

Model evaluation using 10-fold cross validation suggested very good predictive performance (AUC = 0.88), with a predictive deviance of 33% (Table 2).

Although the initial HMM constructed from plant specific sequences performed well in test set discrimination, jack-knife testing by leave-one-out cross validation suggested that this model might have difficulty generalizing to a broader data set.

We had the advantage of profiling a very large number of CpG loci in paired CRC and normal colonic mucosa tissue, and our 2-level cross validation suggested that the four markers could be used as biomarkers with slightly better test characteristics.

Cross validation suggests the model can be used to estimate MRTs in ungauged catchments in similar regions of throughout the Loess Plateau.

Cross validation suggests that these genes need to be tested as potential markers of HSCs and may have functionally important roles in hematopoiesis.

The results of our internal-external cross validation suggests there is limited heterogeneity of the discrimination of the MSI model across the nine geographical regions of Queensland.

Though our cross validation suggests that five iterations is an appropriate training length for our data, this result may be dependent on the number of unlabeled and labeled microarrays and the organisms being compared and so will not generalize for other comparison studies.

A third-order polynomial, the purple function within the green background and having the same decline over the complication and development, showed some promise as a successful model (t[96] = 4.94, p < .0001, adjusted R 2 = .42), but cross-validation suggested that this result was due to overfitting.

Cross-validation suggested that all models achieved good CCC agreement between predicted and observed cases, with low RMSE values (table 2).

In the unadjusted analysis all five cross-validations suggest that the classification scheme classifies individuals significantly better than random (P-values <0.05) and in the adjusted analysis the classification is significantly better in all but one cross-validation experiment (Table 5).