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Categorical data: Difference between revisions
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Text replacement - "Category:Machine learning terms" to "Category:Machine learning terms Category:not updated"
(Created page with "===Introduction== Categorical data is a type of machine learning information that represents qualitative or nominal features rather than numerical or continuous values. 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; this article will give an extensive explanation of categorical data t...") |
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{{see also|Machine learning terms}} | |||
==Introduction== | |||
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. | |||
==Types of Categorical Data== | ==Types of Categorical Data== | ||
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==Representation of Categorical Data== | ==Representation of Categorical Data== | ||
Categorical data is commonly represented with one-hot encoding, which transforms each category value into a binary vector of 0s and 1s. Each binary vector has the same length as the number of categories, with 1 being placed at each position corresponding to that category. For instance, if we have categorical variable representing car colors (red, blue, and green), one-hot encoding could read red = [1, 0, | Categorical data is commonly represented with [[one-hot encoding]], which transforms each category value into a binary vector of 0s and 1s. Each binary vector has the same length as the number of categories, with 1 being placed at each position corresponding to that category. For instance, if we have categorical variable representing car colors (red, blue, and green), one-hot encoding could read red = [1, 0, 0], blue = [0, 1, 0], and green = [0, 0, 1]. | ||
==Applications of Categorical Data== | ==Applications of Categorical Data== | ||
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==Explain Like I'm 5 (ELI5)== | ==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. | 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]] | |||