`svm_linear()`

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

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

## Arguments

- 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.

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

- margin
A positive number for the epsilon in the SVM insensitive loss function (regression only)

## Details

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_linear(argument = !!value)
```

## Examples

```
show_engines("svm_linear")
#> # A tibble: 4 × 2
#> engine mode
#> <chr> <chr>
#> 1 LiblineaR classification
#> 2 LiblineaR regression
#> 3 kernlab classification
#> 4 kernlab regression
svm_linear(mode = "classification")
#> Linear Support Vector Machine Model Specification (classification)
#>
#> Computational engine: LiblineaR
#>
```