Radial basis function support vector machinesSource:
svm_rbf() defines a support vector machine model. For classification,
the model tries to maximize the width of the margin between classes using a
nonlinear class boundary. For regression, the model optimizes a robust loss
function that is only affected by very large model residuals and uses
nonlinear functions of the predictors. The function can fit classification
and regression models.
There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.
More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".
A single character string specifying what computational engine to use for fitting. Possible engines are listed below. The default for this model is
A positive number for the cost of predicting a sample within or on the wrong side of the margin
A positive number for radial basis function.
A positive number for the epsilon in the SVM insensitive loss function (regression only)
This function only defines what type of model is being fit. Once an engine
is specified, the method to fit the model is also defined. See
set_engine() for more on setting the engine, including how to set engine
The model is not trained or fit until the
fit() function is used
with the data.
value <- 1 svm_rbf(argument = !!value)
show_engines("svm_rbf") #> # A tibble: 4 × 2 #> engine mode #> <chr> <chr> #> 1 kernlab classification #> 2 kernlab regression #> 3 liquidSVM classification #> 4 liquidSVM regression svm_rbf(mode = "classification", rbf_sigma = 0.2) #> Radial Basis Function Support Vector Machine Model Specification (classification) #> #> Main Arguments: #> rbf_sigma = 0.2 #> #> Computational engine: kernlab #>