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Model acceptability was quantified with Deviance Information Criterion DICC), which penalized model accuracy by the number of retrieved parameters.
Thus, dimensionality reduction is used to train machine learning algorithms faster as well as increase model accuracy by reducing model overfitting [24].
It has also been suggested that increasing model accuracy by including more terms in a truncated beam model worsens the controller's ability to stabilize the system dynamics.
These approaches succeeded to enhance the model accuracy by adding few points from both the low-fidelity and high-fidelity simulations.
All the three methods produce consistent results after outlier elimination, which improves the regression quality by about 20% and the model accuracy by up to 30%.
Likewise, [48] investigated on the use of random trees in their work and the scholars were able to achieve high levels of model accuracy by modifying the parameters of the random tree classifier.
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Results are presented from a study designed to evaluate the impact upon urban area transport and dispersion (T&D) modeling accuracy by coupling a microscale computational fluid dynamics (CFD) model with a mesoscale numerical weather prediction (NWP) model.
It was expected that the advantage in model accuracy achieved by increasing r = 1 to r = 2 would diminish as n increased, but the boost in accuracy was remarkably consistent.
The model accuracy is verified by experiment data.
Model accuracy was assessed by leave-one-out cross-validation (Arlot and Celisse 2010).
Model accuracy is evaluated by using the area under the curve (AUC).
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