In statistics and machine learning , discretization refers to the process of converting or partitioning continuous attributes , features or variables to discretized or nominal attributes/features/variables/ intervals . This can be useful when creating probability mass functions – formally, in density estimation . It is a form of discretization in general and also of binning , as in making a histogram . Whenever continuous data is discretized, there is always some amount of discretization error . The goal is to reduce the amount to a level considered negligible for the modeling purposes at hand.
5-434: MDL may refer to: Computing [ edit ] Fayyad & Irani's MDL method , a discretization method MDL (programming language) , derived from LISP The extension for Valve 's Source , Source 2 and ID Software 's IDTech game engines proprietary model file format Microsoft Design Language , a design language created by Microsoft Minimum description length ,
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20-433: Is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies). Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method, which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others Many machine learning algorithms are known to produce better models by discretizing continuous attributes. This
25-477: The title MDL . If an internal link led you here, you may wish to change the link to point directly to the intended article. Retrieved from " https://en.wikipedia.org/w/index.php?title=MDL&oldid=1218779899 " Category : Disambiguation pages Hidden categories: Short description is different from Wikidata All article disambiguation pages All disambiguation pages Discretization of continuous features Typically data
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