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Different statistical errors functions are used to evaluate models and determine the most accurate one.
This dataset was used to evaluate models for ranking detected cavities and to determine a threshold above which detected cavities constitute potential binding sites.
EQ-5D states 11111, 33333 and 'Dead' were added to each set as anchors, along with two further 'hold-out' states which were not included in the regression analysis but were used to evaluate models produced by the analysis.
We illustrate the use of the package with two simulated datasets, one under the true model and one with different parameter values, to show how npde can be used to evaluate models.
Likelihood ratio tests were used to evaluate models with the interaction terms compared with models without the interaction terms and reviewed for trends.
10-fold cross-validation approach was used to evaluate models created by different machine learning, including ANN, LR, SVM, and RF.
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The RMS error is widely used to evaluate model performance.
Structural equation modelling was used to evaluate model fit, as well as direct and indirect pathways.
Cross-validation procedure was used to evaluate model performance and uncertainty.
Both the root mean square error (RMS) and the relative error are used to evaluate model performance.
Finally, whey protein isolate was used to evaluate model parameters in real conditions.
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