In tidymodels/parsnip: A Common API to Modeling and Analysis Functions

#| child: aaa.Rmd
#| include: false

r descr_models("svm_linear", "LiblineaR")

Tuning Parameters

#| label: LiblineaR-param-info
#| echo: false
defaults <- 
  tibble::tibble(parsnip = c("cost", "margin"),
                 default = c("1.0", "no default"))

param <-
  svm_linear() |> 
  set_engine("LiblineaR") |> 
  make_parameter_list(defaults)

This model has r nrow(param) tuning parameters:

#| label: LiblineaR-param-list
#| echo: false
#| results: asis
param$item

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

Parsnip changes the default range for cost to c(-10, 5).

Translation from parsnip to the original package (regression)

#| label: LiblineaR-reg
svm_linear(
  cost = double(1),
  margin = double(1)
) |>  
  set_engine("LiblineaR") |> 
  set_mode("regression") |> 
  translate()

Translation from parsnip to the original package (classification)

#| label: LiblineaR-cls
svm_linear(
  cost = double(1)
) |> 
  set_engine("LiblineaR") |> 
  set_mode("classification") |> 
  translate()

The margin parameter does not apply to classification models.

Note that the LiblineaR engine does not produce class probabilities. When optimizing the model using the tune package, the default metrics require class probabilities. To use the tune_*() functions, a metric set must be passed as an argument that only contains metrics for hard class predictions (e.g., accuracy).

Preprocessing requirements

#| child: template-makes-dummies.Rmd

#| child: template-same-scale.Rmd

Case weights

#| child: template-no-case-weights.Rmd

Sparse Data

#| child: template-uses-sparse-data.Rmd

Examples

The "Fitting and Predicting with parsnip" article contains examples for svm_linear() with the "LiblineaR" engine.

References

• Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.

tidymodels/parsnip documentation built on June 2, 2025, 8:10 a.m.