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Optional attributes were assumed not to be needed for data collection as they were not set to mandatory in the archetype.
An attribute was not used in the training process if it was used in generating the specific positive set.
When an attribute is not selected by a respondent the RI of this attribute is set to zero for this individual.
If the patient does not have an attribute marked the attribute is not taken into consideration.
In this set, the domains contrasted level (1) (i.e. the attribute was not present) with level (3) (i.e. it was present to a large extent).
Attributes are not restricted to a finite set of values and they may be of any kind of numerical value and textual string.
However, decision trees are unstable (i.e., variations in the training data can produce different set of attributes to be chosen) and generally multiple output attributes are not allowed.
But perhaps one of the strongest attributes of this book is not what it sets out to do, but what Zuckoff informs readers at the very beginning that the books is not about.
The attribute set in IvIS is not static but rather dynamically changing over time with the collection of new information, which results in the continuous updating of rough approximations for rough set-based data analysis.
Although the benchmark test sets are designed to represent many different alignment problems, the sampling of the four attributes described here is not always homogeneous.
A construct is not restricted to one set of observable indicators or attributes.
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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