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In order to evaluate the model stability, we conducted 4 RF simulations for each dataset.
As an additional measure of model stability, we fit seven identical log-linear models with each model omitting data from one of the sites.
To assess the model stability, we employed a random-effects model with sites assumed to be randomly selected from a larger set of populations.
To assess model stability, we ran diagnostics (dfbetas) that did not suggest the existence of influential cases, and variance inflation factors indicated that there was no collinearity between variables [Field, 2005; Zuur et al., 2009, 2010].
To study the effects of accounting for missing data and incorporating model stability we used a large clinical data set in which we empirically evaluated different methods of deriving a prognostic model.
For the sake of model stability we adopted an entirely data-driven model building approach which is based on two variable reduction and selection steps (maximizing RFI and minimizing the OOB prediction error by bootstrapping), and a final cross-validated ascending variable introduction strategy, in order to determine the ultimate prediction model [ 21, 22].
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Validation of supervised methods is vital to test model stability, and we have employed the following procedure before [ 17].
If we are able to demonstrate the model's stability we increase its public health impact.
(4) Lastly, we test for model stability and residual analysis to check for normality, autocorrelation and heteroscedasticity of the models residuals in the fourth step.
Thus, we go for estimating model stability to ensuring acceptability of both long run and short run coefficient of the study.
In response, we introduce TopicCheck, an interactive tool for assessing topic model stability.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com