parsnip (version 0.1.6)

svm_rbf: General interface for radial basis function support vector machines

Description

svm_rbf() 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.

• rbf_sigma: The precision parameter for the radial basis function.

• 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.

Usage

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

Arguments

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

rbf_sigma

A positive number for radial basis function.

margin

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

Engine Details

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:

kernlab

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

## Radial Basis Function Support Vector Machine Specification (regression)
## 
## Computational engine: kernlab 
## 
## Model fit template:
## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "rbfdot")

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

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

fit() passes the data directly to kernlab::ksvm() so that its formula method can create dummy variables as-needed.

Parameter translations

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	liquidSVM
cost	C (1)	lambdas
rbf_sigma	sigma (varies)	gammas
margin	epsilon (0.1)	NA

Details

The model can be created using the fit() function using the following engines:

• R: "kernlab" (the default)

See Also

fit(), set_engine(), update()

Examples

# NOT RUN {
show_engines("svm_rbf")

svm_rbf(mode = "classification", rbf_sigma = 0.2)
# Parameters can be represented by a placeholder:
svm_rbf(mode = "regression", cost = varying())
# }