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 binning, as in making a histogram.

Typically data is discretized into partitions of K equal lengths/width (equal intervals) or K% of the total data (equal frequencies).

Some mechanisms for discretizing continuous data include:

• Fayyad & Irani's MDL method - Uses Information Gain to recursively define the best bins.
• And many more

Many Machine Learning algorithms are known to produce better models by discretizing continuous attributes

See also

• Data binning
• Density estimation
• Discretization error
• Histogram
• Continuity correction