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

Usage

# S3 method for class 'ksvm'
pmml(
  model,
  model_name = "SVM_model",
  app_name = "R PMML Generator - Package pmml",
  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

if (FALSE) { # \dontrun{
# Train a support vector machine to perform classification.
library(kernlab)

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

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