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The topographic model also performed well, classifying correctly 81.39% of cases validated by the same methodology.
The AE, SW, SE, and LI model also performed comparatively better.
In addition, the SVR-RFFA model also performed well based on other goodness-of-fit statistics such as BIAS (mean bias) and BIASr (relative BIAS).
The Dinuc grammar, which differs from the PFOLD grammar only in its null model, also performed better on rRNA.
The Rx-MGs model also performed better than other diagnosis-based alternative models in this study.
However, in a model with random slopes for time a Poisson mixed model also performed remarkably well.
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Moreover, the Laplace distribution of the OSTAR-Laplace model also performs fat-tail effect naturally.
Moreover, the local and general formulas of the most accurate temperature-based model also perform better than the two most accurate sunshine-based models from the literature.
The significant superiority of the ETAS model in forecasting seismcity to the Poisson model in binomial information score implies that the ETAS model also performs better than the Poisson model in the Poisson information score.
Here, we demonstrate that the model also performs well when approach is the chief reaction.
Models developed using extended family history and genetic data, such as the IBIS model, also perform well in women considered at average risk (for example, no family history of breast cancer, BRCA1/2 mutation negative).
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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