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Discrete feature: Difference between revisions
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(Created page with "===Introduction== Machine learning uses features, or characteristics or attributes of input data, as a basis for making predictions or decisions. Discrete features (also referred to as categorical features) are those which take on a limited set of values rather than providing an infinite range of values. ==Definition== Discrete features refer to data elements whose values fall outside a finite or infinite set. Examples of discrete features include gender, hair color, oc...") |
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===Introduction== | ===Introduction== | ||
Machine learning uses features, or characteristics or attributes of input data, as a basis for making predictions or decisions. Discrete | Machine learning uses [[features]], or characteristics or attributes of [[input data]], as a basis for making predictions or decisions. [[Discrete feature]]s (also referred to as [[categorical feature]]s) are those which take on a limited set of values rather than providing an infinite range of values. For example, a [[feature]]s with values such as types of cars, types of animals and plants or types of food. Discrete feature is the opposite of [[continuous feature]]. | ||
==Definition== | ==Definition== | ||
Discrete features refer to data elements whose values fall outside a finite or infinite set. Examples of discrete features include gender, hair color, occupation and zip code | Discrete features refer to data elements whose values fall outside a finite or infinite set. Examples of discrete features include gender, hair color, occupation and zip code. | ||
Discrete features differ from continuous ones, which can take any value within a specified range. Examples of continuous characteristics include age, height and temperature. | Discrete features differ from continuous ones, which can take any value within a specified range. Examples of continuous characteristics include age, height and temperature. | ||