parsnip (version 0.1.6)

svm_linear: General interface for linear support vector machines

Description

svm_linear() 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.

• 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_linear(mode = "unknown", cost = 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

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:

LiblineaR

svm_linear() %>% 
  set_engine("LiblineaR") %>% 
  set_mode("regression") %>% 
  translate()

## Linear Support Vector Machine Specification (regression)
## 
## Computational engine: LiblineaR 
## 
## Model fit template:
## LiblineaR::LiblineaR(x = missing_arg(), y = missing_arg(), wi = missing_arg(), 
##     type = 11, svr_eps = 0.1)

svm_linear() %>% 
  set_engine("LiblineaR") %>% 
  set_mode("classification") %>% 
  translate()

## Linear Support Vector Machine Specification (classification)
## 
## Computational engine: LiblineaR 
## 
## Model fit template:
## LiblineaR::LiblineaR(x = missing_arg(), y = missing_arg(), wi = missing_arg(), 
##     type = 1)

Note that the LiblineaR engine cannot produce class probabilities. When optimizing the model using the tune package, the default metrics require class probabilities. To be able to use the tune_*() functions, a metric set must be passed as an argument and it can only contain metrics associated with hard class predictions (e.g., accuracy and so on).

This engine fits models that are L2-regularized for L2-loss. In the LiblineaR documentation, these are types 1 (classification) and 11 (regression).

kernlab

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

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

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

## Linear Support Vector Machine Specification (classification)
## 
## Computational engine: kernlab 
## 
## Model fit template:
## kernlab::ksvm(x = missing_arg(), data = missing_arg(), kernel = "vanilladot", 
##     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	LiblineaR	kernlab
cost	C (1)	C (1)
margin	svr_eps (0.1)	epsilon (0.1)

Details

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

• R: "LiblineaR" (the default) or "kernlab"

See Also

fit(), set_engine(), update()

Examples

# NOT RUN {
show_engines("svm_linear")

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