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Is anyone aware of a package that implements a supervised learning algorithm for the discretization of continuous variables?
The dprep package contained functions along this line, but the package has been deprecated. Any ideas?
Thanks for your help!
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Discretization methods are used to chop a continuous function (i.e., the real solution to a system of differential equations in CFD) into a discrete function, where the solution values are defined at each point in space and time. Discretization simply refers to the spacing between each point in your solution space.
In applied mathematics, discretization is the process of transferring continuous functions, models, variables, and equations into discrete counterparts. This process is usually carried out as a first step toward making them suitable for numerical evaluation and implementation on digital computers.
Discretization is the process through which we can transform continuous variables, models or functions into a discrete form. We do this by creating a set of contiguous intervals (or bins) that go across the range of our desired variable/model/function. Continuous data is Measured, while Discrete data is Counted.
Discretization acts as a variable selection method in addition to transforming the continuous values of the variable to discrete ones.
You can still use the dprep package, but you have to install it from source (I just tested and it works well). However, you may well have a look at the discretization or infotheo packages which provide similar functionalities, e.g. equal interval width, equal frequency intervals, ChiMerge, etc.
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