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This implies that the ANN models had higher errors associated with their better predictions.
The statistical results clearly illustrated that the consensus models had higher predictive accuracy ((q_{ext}^{2}) = 0.669 0.689) than any individual model.
Classification tree models had higher classification accuracy with the training data, but were less robust when used for predictions with the test data.
We found that the dental extraction and bunionectomy models had higher assay sensitivity than the joint replacement and soft tissue surgery models.
Results indicated that among the new proposed models, ELM and GANN models were much better than WNN model, and the temperature-based ELM and GANN models had better performance than Hargreaves and modified Hargreaves models, radiation-based ELM and GANN models had higher precision than Makkink, Priestley Taylor and Ritchie models.
All more complicated models had higher Akaike information criterion (AIC) scores.
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All models had high NPVs (≥97%).
Our linear covariance models had high determination coefficients, but the values obtained were always lower than 1.
Mice from the AxD models had high-enough levels of GFAP to allow dilutions from individual samples, typically 1 16 1 120.
The multivariate models had high levels of specificity for patients who would have a hospital re-attendance at one-year (92 % specific) and three-years (97%% specific).
Cross-validation results demonstrated that the models had high predictive accuracy (cross-validation R values of 0.59, 0.76, and 0.77 for PM10, pre-1999 PM2.5, and post-1999 PM2.5, respectively).
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