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This model generated four important estimates: 1.
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The model generated three different, but plausible land use patterns for 2025.
The first model generated two genes (ie, questions), with variables 90 and 59, while the second model generated three genes with variables 90, 59, and 60.
This model generated nine rules with 71.29% ± 11.52% and 79.31% ± 13.25% for accuracy and precision, respectively.
This model generated six rules, with an accuracy of 66.62% ± 10.24% and a precision of 69.24% ± 9.35%.
After selecting 40 as the optimal number of topics representing this dataset, the topic model generated two probabilistic distributions.
The fitness model generates two ecological patterning predictions.
The survival model generates two results necessary to complete the treatment cost calculation, equation (2).
The Rasch model generates two estimates, called person location (or logit) and item location (or logit), which are nonlinear (log odds) transformations of raw scores.
Two residues are considered neighbors if, in at least 50% of the models generated, one of their heavy atom distances is smaller than 4 Å, in order to focus on the first shell of residues around the SSE.
Figure 3 depicts the C4.5 model, which used six variables (the five common variables and the MAP, which is not included in the LR model) and generated ten decision rules.
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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