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For example, most problems use data vectors with discrete, real-valued and nominal feature values.
We also introduce a metric on TET feature values.
Further, feature values are negative in this example.
Thus, the feature values can be understood as confidence values.
A χ2-test is exercised for nominal feature values [37].
A total of 131 feature values are calculated.
The feature values in Figures 5 and 6 are normalized.
Then the feature values within each grid are averaged.
The feature values are ratios in the range [ 0,1].
This observation, plus a comparison of the lexical overhead of naming unique combinations of feature values rather than the feature values themselves, helps explain the rarity of paradigms.
This key step is carried out considering the distributions of feature values across all the classes.
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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