Discretization of continuous features

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).{{Cite journal

| last1 = Clarke | first1 = E. J.

| last2 = Barton | first2 = B. A.

| doi = 10.1002/(SICI)1098-111X(200001)15:1<61::AID-INT4>3.0.CO;2-O

| title = Entropy and MDL discretization of continuous variables for Bayesian belief networks

| journal = International Journal of Intelligent Systems

| volume = 15

| pages = 61–92

| year = 2000

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| url=http://sci2s.ugr.es/keel/pdf/specific/articulo/IJIS00.pdf |accessdate=2008-07-10

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Mechanisms for discretizing continuous data include Fayyad & Irani's MDL method,Fayyad, Usama M.; Irani, Keki B. (1993) {{cite web|hdl=2014/35171 | url = https://www.ijcai.org/Proceedings/93-2/Papers/022.pdf | title = Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning| date = 29 July 2023 }}, Proc. 13th Int. Joint Conf. on Artificial Intelligence (Q334 .I571 1993), pp. 1022-1027 which uses mutual information to recursively define the best bins, CAIM, CACC, Ameva, and many othersDougherty, J.; Kohavi, R.; Sahami, M. (1995). "[http://robotics.stanford.edu/users/sahami/papers-dir/disc.pdf Supervised and Unsupervised Discretization of Continuous Features]". In A. Prieditis & S. J. Russell, eds. Work. Morgan Kaufmann, pp. 194-202

Many machine learning algorithms are known to produce better models by discretizing continuous attributes.{{cite journal| first1=S. |last1=Kotsiantis |first2= D| last2= Kanellopoulos |title=Discretization Techniques: A recent survey|journal= GESTS International Transactions on Computer Science and Engineering |volume=32 |issue=1 |year=2006 |pages= 47–58|citeseerx = 10.1.1.109.3084}}