Sentence examples for predictive value of the models from inspiring English sources

This is to establish the predictive value of the models, since validating the models on the same year they were learned would not have yielded any information about the predictive performance of the models on new unseen data.

Area under the receiver operating curve (AUC) was calculated to determine the predictive value of the models (0.5 0.7 is poor, 0.7 0.9 is good, and >0.9 is excellent).

Remarkably, when linear regression analyses, containing both serum markers and scoring methods, were performed, results demonstrated that serum markers did not increase the predictive value of the models for cartilage and bone damage.

Following model construction, cross validation of the data is employed to confirm the predictive value of the model.

Integration of the model with data from uncored wells shows that spatial variability may in some areas occur on distances smaller than current core-spacing (450 m), diminishing the geometric predictive value of the model in these areas.

Prediction of the level of tracer metabolites in the brain is not straightforward; however, it appears to not deteriorate the predictive value of the model, which is consistent with metabolites generally having more hydrogen bond acceptors and therefore increased probability of being effluxed at the blood-brain barrier.

The analysis may not be necessarily limited to psychological parameters and other potentially useful variables could be tested to improve the predictive value of the model.

Stepwise multiple regression analysis to predict VO2max from LogEEPAwalking, LogEEPAmoderate, LogEEPAvigorous, LogEEPAtotal, age and percentage of body fat (%fat), indicated that the %fat explained 62% of the variance in VO2max and the age another 10%, while the other variables did not improve the predictive value of the model.

Seven protein variables accounted for the majority of the predictive value of the model.

The negative predictive value of the model scores varied from nearly 100%to94%4%.

This cut-off was chosen as clinically relevant with the priority to increase the negative predictive value of the model.