Does ADAPA support all modeling techniques specified in PMML?

We are constantly working towards that goal. Currently, ADAPA supports several PMML elements, including pre- and post-processing elements.

As for modeling techniques it supports the following PMML elements:

• Neural Networks


• Support Vector Machines


• Regression


• General Regression


• Decision Trees

If you are interested in using ADAPA but the PMML element you use is not listed in here, feel free to contact us.

If you are unsure about what a PMML element represents, please check the DMG (Data Mining Group) webpage which defines PMML 3.2 (the latest version of PMML). Also, take a look at the Zementis ADAPA Predictive Analytics page which contains a list of modeling techniques supported by the PMML elements listed above and ADAPA.

What types of regression models built with R can I export to PMML?

Quick answer:

• Linear Regression
  
• Binary Logistic Regression
  

You can basically export any linear regression models you build using the R glm function and the gaussian family into PMML 3.2 by using the PMML package available from Togaware. See link below:

http://rattle.togaware.com/

The PMML package is also available through CRAN.

The function to be used is named pmml.lm. The original version only allowed for the exporting of linear regression models. We have extended it to also export binary logistic regression models built using the R function glm and the binomial family.

The following example trains a binary logistic regression model for the audit dataset and exports its equivalent PMML 3.2 code:

audit <- read.csv(file("http://rattle.togaware.com/audit.csv"))
binlog <- glm(Adjusted ~ ., data=crs$dataset[crs$sample,c(2:8,10:13)], family=binomial(logit))
pmml.lm(binlog)

Note that this function does not support multinomial logistic regression models or any other regression models built using the VGAM package.