pmml (version 2.5.2)

pmml.ksvm: Generate the PMML representation for a ksvm object from the package kernlab.

Description

Generate the PMML representation for a ksvm object from the package kernlab.

Usage

# S3 method for ksvm
pmml(
  model,
  model_name = "SVM_model",
  app_name = "SoftwareAG PMML Generator",
  description = "Support Vector Machine Model",
  copyright = NULL,
  model_version = NULL,
  transforms = NULL,
  missing_value_replacement = NULL,
  dataset = NULL,
  ...
)

Arguments

model

A ksvm object.

model_name

A name to be given to the PMML model.

app_name

The name of the application that generated the PMML.

description

A descriptive text for the Header element of the PMML.

copyright

The copyright notice for the model.

model_version

A string specifying the model version.

transforms

Data transformations.

missing_value_replacement

Value to be used as the 'missingValueReplacement' attribute for all MiningFields.

dataset

Data used to train the ksvm model.

...

Further arguments passed to or from other methods.

Value

PMML representation of the ksvm object.

Details

Both classification (multi-class and binary) as well as regression cases are supported.

The following ksvm kernels are currently supported: rbfdot, polydot, vanilladot, tanhdot.

The argument dataset is required since the ksvm object does not contain information about the used categorical variable.

References

kernlab: Kernel-based Machine Learning Lab (on CRAN)

Examples

# NOT RUN {
# Train a support vector machine to perform classification.
library(kernlab)

model <- ksvm(Species ~ ., data = iris)

model_pmml <- pmml(model, dataset = iris)
# }