A PMML package for R that exports all kinds of predictive models is available directly from CRAN.

The * pmml* package offers support for the following model types:

- ksvm (kernlab): Support Vector Machines
- nnet: Neural Networks
- rpart: C&RT Decision Trees
- lm & glm (stats): Linear and Binary Logistic Regression Models
- arules: Association Rules
- kmeans and hclust: Clustering Models
- multinom (nnet): Multinomial Logistic Regression Models
- glm (stats): Generalized Linear Models for classification and regression with a wide variety of link functions
- randomForest: Random Forest Models for classification and regression
- coxph (survival): Cox Regression Models to calculate survival and stratified cumulative hazards
- naiveBayes (e1071): Naive Bayes Classifiers
- glmnet: Linear ElasticNet Regression Models
- ada: Stochastic Boosting
- svm (e1071): Support Vector Machines

*package can also export data transformations built with the*

**pmml***package (see below). It can also be used to merge two disctinct PMML files into one. For example, if transformations and model were saved into separate PMML files, it can combine both files into one, as described in Chapter 5 of the PMML book - PMML in Action.*

**pmmlTransformations**### How does it work?

**Documentation**

For more on the * pmml* package, please take a look at the paper we published in The R Journal. For that, just follow the link below:

1) Paper: PMML: An Open Standard for Sharing Models

Also, make sure to check out the package's documentation from CRAN:

2) CRAN: pmml Package

## R PMML Transformations Package

*, this package transforms data and when used in conjunction with the*

**pmmlTranformations***package, it allows for data transformations to be exported together with the predictive model in a single PMML file. Transformations currently supported are:*

**pmml**- Min-max normalization
- Z-score normalization
- Dummy-fication of categorical variables
- Value Mapping
- Discretization (binning)
- Variable renaming

**package, please feel free to contact us.**

*pmmlTransformations***How does it work?**

*pmmlTransformations*package works in tandem with the

*pmml*package so that data pre-processing can be represented together with the model in the resulting PMML code.

- 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

**Documentation**

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

## 0 Comments