Categorical data: Difference between revisions

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+{{see also|Machine learning terms}}
 ==Introduction==
-In [[machine learning]], categorical data represents qualitative or nominal [[features]] rather than numerical or [[continuous features]]. It often represents attributes or characteristics of objects or events which cannot be quantified quantitatively. Categorical data plays an essential role in many machine learning tasks such as [[classification]], [[clustering]] and [[regression]].
+In [[machine learning]], categorical data represents qualitative or nominal [[feature]]s rather than numerical or [[continuous feature]]s. It often represents attributes or characteristics of objects or events which cannot be quantified quantitatively. Categorical data plays an essential role in many machine learning tasks such as [[classification]], [[clustering]] and [[regression]].
 Categorical data is sometimes known as [[discrete feature]]s.
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 ==Explain Like I'm 5 (ELI5)==
 Categorical data is like different kinds of candy. Nominal candy looks like different colors of M&M's with no order; ordinal candy has an established hierarchy from small to large. We use categorical data in computer programs to understand things that cannot be quantified numerically - such as what something is, group similar items together, or estimate how much something costs based on other similar things.
+[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]

Categorical data (view source)