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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.

Typically data is discretized into partitions of *K* equal lengths/width (equal intervals) or K% of the total data (equal frequencies).^{[1]}

Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method,^{[2]} which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many others^{[3]}

Many machine learning algorithms are known to produce better models by discretizing continuous attributes.^{[4]}

This is a partial list of software that implement MDL algorithm.

**^**Clarke, E. J.; Barton, B. A. (2000). "Entropy and MDL discretization of continuous variables for Bayesian belief networks" (PDF).*International Journal of Intelligent Systems*.**15**: 61–92. doi:10.1002/(SICI)1098-111X(200001)15:1<61::AID-INT4>3.0.CO;2-O. Retrieved 2008-07-10.**^**Fayyad, Usama M.; Irani, Keki B. (1993) "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning" (PDF). hdl:2014/35171.,*Proc. 13th Int. Joint Conf. on Artificial Intelligence*(Q334 .I571 1993), pp. 1022-1027**^**Dougherty, J.; Kohavi, R. ; Sahami, M. (1995). "Supervised and Unsupervised Discretization of Continuous Features". In A. Prieditis & S. J. Russell, eds.*Work*. Morgan Kaufmann, pp. 194-202**^**Kotsiantis, S.; Kanellopoulos, D (2006). "Discretization Techniques: A recent survey".*GESTS International Transactions on Computer Science and Engineering*.**32**(1): 47–58. CiteSeerX 10.1.1.109.3084.