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This model classifies individuals to be susceptible, infectious, or removed and permanently immune.
This model classifies most preferred user to least preferred user for the given information using fuzzy score.
This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001).
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This modeling classifies metallic alloys in two groups for damage accumulation, with a stable (independent to pre-hardening) CSSC as for aluminum alloys and with an unstable (dependent to pre-hardening) one as for austenitic stainless steels.
At the same time, the model classifies DMUs and ranks the fully efficient ones.
On the other hand, the model classifies 83.33% of these compounds in the predicting series.
In our case, the model classifies tweets into positive and negative sub-classes.
On the other hand, the model classifies 100.00% of these compounds in the test set.
On the test set, the model classifies correctly 18 out of 19 cases.
Using an independent data set from the same study area, the model classified correctly 88% of sample plots.
Sensitivity and specificity were considered to evaluate how well the model classified presences and absences respectively.
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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