`svm_poly()`

is a way to generate a *specification* of a model
before fitting and allows the model to be created using
different packages in R or via Spark. The main arguments for the
model are:

`cost`

: The cost of predicting a sample within or on the wrong side of the margin.`degree`

: The polynomial degree.`scale_factor`

: A scaling factor for the kernel.`margin`

: The epsilon in the SVM insensitive loss function (regression only)

These arguments are converted to their specific names at the
time that the model is fit. Other options and arguments can be
set using `set_engine()`

. If left to their defaults
here (`NULL`

), the values are taken from the underlying model
functions. If parameters need to be modified, `update()`

can be used
in lieu of recreating the object from scratch.

svm_poly( mode = "unknown", cost = NULL, degree = NULL, scale_factor = NULL, margin = NULL ) # S3 method for svm_poly update( object, parameters = NULL, cost = NULL, degree = NULL, scale_factor = NULL, margin = NULL, fresh = FALSE, ... )

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

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) |

object | A polynomial SVM model specification. |

parameters | A 1-row tibble or named list with |

fresh | A logical for whether the arguments should be modified in-place of or replaced wholesale. |

... | Not used for |

The model can be created using the `fit()`

function using the
following *engines*:

R:

`"kernlab"`

(the default)

Engines may have pre-set default arguments when executing the model fit call. For this type of model, the template of the fit calls are below:

svm_poly() %>% set_engine("kernlab") %>% set_mode("regression") %>% translate()

## Polynomial Support Vector Machine Specification (regression) ## ## Computational engine: kernlab ## ## Model fit template: ## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "polydot")

svm_poly() %>% set_engine("kernlab") %>% set_mode("classification") %>% translate()

## Polynomial Support Vector Machine Specification (classification) ## ## Computational engine: kernlab ## ## Model fit template: ## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "polydot", ## prob.model = TRUE)

`fit()`

passes the data directly to `kernlab::ksvm()`

so that its
formula method can create dummy variables as-needed.

The standardized parameter names in parsnip can be mapped to their original names in each engine that has main parameters. Each engine typically has a different default value (shown in parentheses) for each parameter.

parsnip | kernlab |

cost | C (1) |

degree | degree (1) |

scale_factor | scale (1) |

margin | epsilon (0.1) |

#> # A tibble: 2 x 2 #> engine mode #> <chr> <chr> #> 1 kernlab classification #> 2 kernlab regressionsvm_poly(mode = "classification", degree = 1.2)#> Polynomial Support Vector Machine Specification (classification) #> #> Main Arguments: #> degree = 1.2 #>#> Polynomial Support Vector Machine Specification (regression) #> #> Main Arguments: #> cost = varying() #>model <- svm_poly(cost = 10, scale_factor = 0.1) model#> Polynomial Support Vector Machine Specification (unknown) #> #> Main Arguments: #> cost = 10 #> scale_factor = 0.1 #>#> Polynomial Support Vector Machine Specification (unknown) #> #> Main Arguments: #> cost = 1 #> scale_factor = 0.1 #>#> Polynomial Support Vector Machine Specification (unknown) #> #> Main Arguments: #> cost = 1 #>