Discretization of continuous features
In statistics and machine learning, discretization refers to the process of converting continuous features or variables to discretized or nominal features. 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 (equal intervals) or K% of the total data (equal frequencies).[1]
Some mechanisms for discretizing continuous data include:
- Fayyad & Irani's MDL method [2] - Uses Information Gain to recursively define the best bins.
- And many more [3]
See also
References
- ^ "Entropy and MDL Discretization of Continuous Variables for Bayesian Belief Networks" (PDF). Retrieved 2008-07-10.
- ^ "Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning". Retrieved 2008-07-10.
- ^ "Supervised and Unsupervised Discretization of Continuous Features". Retrieved 2008-07-10.
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