pmml.ksvm {pmml} | R Documentation |
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 class '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. |
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.
Value
PMML representation of the ksvm object.
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)
## End(Not run)