man/rmd/svm_linear_LiblineaR.md

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

For this engine, there are multiple modes: classification and regression

Tuning Parameters

This model has 2 tuning parameters:

• cost: Cost (type: double, default: 1.0)

• margin: Insensitivity Margin (type: double, default: no default)

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)

svm_linear(
  cost = double(1),
  margin = double(1)
) %>%  
  set_engine("LiblineaR") %>% 
  set_mode("regression") %>% 
  translate()

## Linear Support Vector Machine Model Specification (regression)
## 
## Main Arguments:
##   cost = double(1)
##   margin = double(1)
## 
## Computational engine: LiblineaR 
## 
## Model fit template:
## LiblineaR::LiblineaR(x = missing_arg(), y = missing_arg(), C = double(1), 
##     svr_eps = double(1), type = 11)

Translation from parsnip to the original package (classification)

svm_linear(
  cost = double(1)
) %>% 
  set_engine("LiblineaR") %>% 
  set_mode("classification") %>% 
  translate()

## Linear Support Vector Machine Model Specification (classification)
## 
## Main Arguments:
##   cost = double(1)
## 
## Computational engine: LiblineaR 
## 
## Model fit template:
## LiblineaR::LiblineaR(x = missing_arg(), y = missing_arg(), C = double(1), 
##     type = 1)

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

Factor/categorical predictors need to be converted to numeric values (e.g., dummy or indicator variables) for this engine. When using the formula method via \code{\link[=fit.model_spec]{fit()}}, parsnip will convert factor columns to indicators.

Predictors should have the same scale. One way to achieve this is to center and scale each so that each predictor has mean zero and a variance of one.

Case weights

The underlying model implementation does not allow for case weights.

Sparse Data

This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the Matrix package and sparse tibbles from the sparsevctrs package are supported. See [sparse_data] for more information.

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 Feb. 19, 2025, 2:10 a.m.