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Binning
Binning or discretization is the process of transforming numerical variables into categorical counterparts. An example is to bin values for Age into categories such as 20-39, 40-59, and 60-79. Numerical variables are usually discretized in the modeling methods based on frequency tables (e.g., decision trees). Moreover, binning may improve accuracy of the predictive models by reducing the noise or non-linearity. Finally, binning allows easy identification of outliers, invalid and missing values of numerical variables.

There are two types of binning, unsupervised and supervised.