R PMML Transformations Package
- Min-max normalization
- Z-score normalization
- Dummy-fication of categorical variables
- Value Mapping
- Discretization (binning)
- Variable renaming
The released package version 1.3 has new functions to translate arbitrary mathematical expressions into a PMML representation. These new functions are:
To use these functions, one simply supplies an arbitrary mathematical expression, written in a normal way, as the input and the equivalent PMML expression is given as the output.
The 'FunctionToPMML' function is meant to be used to get a quick look at the PMML expressions output while the 'FunctionXform' function is meant to be used just as the other pmmlTransformations functions, as described below, to add those expressions to the 'LocalTransformations' element automatically.
For more information, simply load the pmmlTransformations library in R and type 'vignettes("FunctionXform")
How does it work?
- With the use of the pmmlTransformations package, transform the raw input data as appropriate
- Use transformed and raw data as inputs to the modeling function/package (hclust, nnet, glm, ...)
- Output the entire solution (data pre-processing + model) in PMML using the pmml package
Example - sequence of R commands used to build a linear regression model using lm with transformed data
For more on the pmmlTransformations package, please take a look at the paper we wrote for the KDD 2013 PMML Workshop. For that, just follow the link below:
1) KDD Paper: The R pmmlTransformations Package
Also, make sure to check out the package's documentation from CRAN:
2) CRAN: pmmlTransformations Package