`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.

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

## Usage

```
svm_poly(
mode = "unknown",
engine = "kernlab",
cost = NULL,
degree = NULL,
scale_factor = NULL,
margin = NULL
)
```

## 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

- degree
A positive number for polynomial degree.

- scale_factor
A positive number for the polynomial scaling factor.

- 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_poly(argument = !!value)
```

## Examples

```
show_engines("svm_poly")
#> # 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
#>
```