`svm_rbf()`

defines a support vector machine model. For classification,
the model tries to maximize the width of the margin between classes.
For regression, the model optimizes a robust loss function that is only
affected by very large model residuals.

This SVM model uses a nonlinear function, specifically the radial basis function, to create the decision boundary or regression line.

There are different ways to fit this model. See the engine-specific pages for more details:

`kernlab (default)`

More information on how parsnip is used for modeling is at https://www.tidymodels.org/.

svm_rbf( mode = "unknown", engine = "kernlab", cost = NULL, rbf_sigma = NULL, margin = NULL )

mode | A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification". |
---|---|

engine | A single character string specifying what computational engine
to use for fitting. Possible engines are listed below. The default for this
model is |

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

rbf_sigma | A positive number for radial basis function. |

margin | 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.

The model is not trained or fit until the `fit.model_spec()`

function is used
with the data.

https://www.tidymodels.org, *Tidy Models with R*

#> # A tibble: 4 × 2 #> engine mode #> <chr> <chr> #> 1 kernlab classification #> 2 kernlab regression #> 3 liquidSVM classification #> 4 liquidSVM regressionsvm_rbf(mode = "classification", rbf_sigma = 0.2)#> Radial Basis Function Support Vector Machine Specification (classification) #> #> Main Arguments: #> rbf_sigma = 0.2 #> #> Computational engine: kernlab #>