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 for the modeling purposes at hand.
Typically data is discretized into partitions of K equal lengths/width or K% of the total data.
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.

Software

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