What does "Class Separation" mean?

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Class separation refers to how well different groups, or classes, of items can be distinguished from one another in a dataset. When different classes are clearly separated, it becomes easier to identify and categorize new items.

In the context of visualizing data, good class separation means that similar items are grouped together, while different items are far apart. This is important because it helps people understand the data better and make more accurate decisions based on it.

For example, in medical images, if images of healthy tissue and images of unhealthy tissue are well separated, it allows doctors to quickly recognize and classify the conditions being viewed. This clarity can improve the way data is analyzed and how findings are shared.