This means that categorical data must be converted to a numerical form. For example, scikit-learn has this requirement. Some implementations of machine learning algorithms require all data to be numerical. In general, this is mostly a constraint of the efficient implementation of machine learning algorithms rather than hard limitations on the algorithms themselves. They require all input variables and output variables to be numeric. Many machine learning algorithms cannot operate on label data directly. Some algorithms can work with categorical data directly.įor example, a decision tree can be learned directly from categorical data with no data transform required (this depends on the specific implementation). Variable comprises a finite set of discrete values with a ranked ordering between values. Variable comprises a finite set of discrete values with no relationship between values. For example, a numerical variable between 1 and 10 can be divided into an ordinal variable with 5 labels with an ordinal relationship: 1-2, 3-4, 5-6, 7-8, 9-10. This type of categorical variable is called an ordinal variable because the values can be ordered or ranked.Ī numerical variable can be converted to an ordinal variable by dividing the range of the numerical variable into bins and assigning values to each bin. The “ place” variable above does have a natural ordering of values. Some categories may have a natural relationship to each other, such as a natural ordering.

A “ place” variable with the values: “ first“, “ second“, and “ third“.Įach value represents a different category.A “ color” variable with the values: “ red“, “ green“, and “ blue“.A “ pet” variable with the values: “ dog” and “ cat“.The number of possible values is often limited to a fixed set.Ĭategorical variables are often called nominal.

Numerical data, as its name suggests, involves features that are only composed of numbers, such as integers or floating-point values.Ĭategorical data are variables that contain label values rather than numeric values. This tutorial is divided into six parts they are: Photo by Felipe Valduga, some rights reserved. Ordinal and One-Hot Encoding Transforms for Machine Learning

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