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svm_poly() defines a support vector machine model. For classification, the model tries to maximize the width of the margin between classes using a polynomial class boundary. For regression, the model optimizes a robust loss function that is only affected by very large model residuals and uses polynomial functions of the predictors. This 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.

¹ The default engine.

More information on how parsnip is used for modeling is at


  mode = "unknown",
  engine = "kernlab",
  cost = NULL,
  degree = NULL,
  scale_factor = NULL,
  margin = NULL



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.


A positive number for the cost of predicting a sample within or on the wrong side of the margin


A positive number for polynomial degree.


A positive number for the polynomial scaling factor.


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 arguments.

The model is not trained or fit until the fit() function is used with the data.

Each of the arguments in this function other than mode and engine are captured as quosures. To pass values programmatically, use the injection operator like so:

value <- 1
svm_poly(argument = !!value)


#> # A tibble: 2 × 2
#>   engine  mode          
#>   <chr>   <chr>         
#> 1 kernlab classification
#> 2 kernlab regression    

svm_poly(mode = "classification", degree = 1.2)
#> Polynomial Support Vector Machine Model Specification (classification)
#> Main Arguments:
#>   degree = 1.2
#> Computational engine: kernlab