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Although the RT sequences in the test-set harbored, on average, eleven mutations, the root mean squared error of prediction (RMSEP) was as low as 0.25, which indicates a good model performance also for heavily mutated virus variants.
Overall model performance also improved: Nagelkerke's R increased from 13% in the base model to 32% for the self-reported model and 39% for the physician-measured model, and the Brier score decreased.
The choice of metric for calculating distances between cases can be another critical point in the design of kNN models, because model performance also depends on the measure of distance used.
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Besides the visual comparison, the model performance is also evaluated using following three statistical measures: 1. Root mean squared error (RMSE): {text{RMSE}} = sqrt {frac{{sumlimits_{i = 1}^{n} {left( {Q_{{e_{i} }} - Q_{{o_{i} }} } right)}^{2} }}{n}} (7) 2.
Differential gene expression was also dependent on the analysis model used [ 19]; interestingly, differences in model performance could also be traced to the practices and experience of the team doing the analysis [ 19].
The impact of increasing number of observations from different sites on model performance is also scrutinised.
In addition, the effects of data transformation on model performance were also investigated.
ANN model performance is also compared with the Flory–Huggins and the modified Flory–Huggins thermo dynamical models.
Model performance was also successfully evaluated for structurally and compositionally complex old-growth forests in the western Cascades of Oregon.
By discarding useless or redundant variables, not only it improves model performance but also facilitates its interpretability.
Further testing at additional sites to calibrate single- and multiple-species growth and identify any spatial trends in model performance will also be beneficial.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com