pmml.ksvm | R Documentation |
Generate the PMML representation for a ksvm object from the package kernlab.
## 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, ... )
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. |
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.
PMML representation of the ksvm object.
kernlab: Kernel-based Machine Learning Lab (on CRAN)
## 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)
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