pmml.ksvm {pmml}R Documentation

Generate PMML for a ksvm object

Description

Generate the PMML representation for a ksvm object (SVM). The ksvm object is converted into a PMML representation. The PMML can then be imported into other systems that accept PMML. With this code, a PMML representation can be obtained for SVMs implementing classification (multi-class and binary) as well as regression.

Usage

## S3 method for class 'ksvm':
pmml(model, model.name="SVM_model", app.name="Rattle/PMML",
     description="Support Vector Machine PMML Model", copyright=NULL,  data.name, ...)

Arguments

model a ksvm object.
data.name the name of the data object used to train the SVM model in ksvm - required since the ksvm object does not appear to record information about the used categorical variables.
model.name a name to give to the model in the PMML.
app.name the name of the application that generated the PMML.
description a descriptive text for the header of the PMML.
copyright the copyright notice for the model.
... further arguments passed to or from other methods.

Details

The generated PMML can be imported into any PMML consuming application that recognizes PMML 3.2. An example is ADAPA. ADAPA (Adaptive Decision and Predictive Analytics) is a lightweight decision engine that offers at its core batch and real-time scoring of predictive models as well as fast execution of business rules. ADAPA supports an extensive collection of PMML elements, including the following predictive techniques: 1) Neural Networks (Backprogagation and Neural Gas); 2) Support Vector Machines; 3) Linear and Logistic Regression as well as all general regression PMML models: a) Multinomial Logistic; b) General Linear; 3) Ordinal Multinomial; 4) Simple Regression; and 5) Generalized Linear Model. ADAPA provides a reliable and fast way to manage, deploy, and execute a multitude of models and decision strategies.

Author(s)

info@zementis.com

References

Package home page: http://rattle.togaware.com

PMML home page: http://www.dmg.org

ADAPA home page: http://www.zementis.com/adapa.htm

See Also

pmml. ksvm.

Examples

# train a support vector machine to perform binary classification
require(kernlab)
audit <- read.csv(file("http://rattle.togaware.com/audit.csv"))
myksvm <- ksvm(as.factor(Adjusted) ~ ., data=audit[,c(2:10,13)], kernel="rbfdot", prob.model=TRUE)
pmml(myksvm, data=audit)

[Package pmml version 1.1.7 Index]