svm_linear() defines a support vector machine model. For classification,
the model tries to maximize the width of the margin between classes (using a
linear class boundary). For regression, the model optimizes a robust loss
function that is only affected by very large model residuals and uses a
linear fit. This function can fit classification and regression models.
There are different ways to fit this model, and the method of estimation is chosen by setting the model engine. The engine-specific pages for this model are listed below.
¹ The default engine.More information on how parsnip is used for modeling is at https://www.tidymodels.org/.
Arguments
- mode
A single character string for the prediction outcome mode. Possible values for this model are "unknown", "regression", or "classification".
- engine
A single character string specifying what computational engine to use for fitting.
- 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)
Details
This function only defines what type of model is being fit. Once an engine
is specified, the method to fit the model is also defined. See
set_engine() for more on setting the engine, including how to set engine
arguments.
The model is not trained or fit until the fit() function is used
with the data.
Each of the arguments in this function other than mode and engine are
captured as quosures. To pass values
programmatically, use the injection operator like so:
value <- 1
svm_linear(argument = !!value)Examples
show_engines("svm_linear")
#> # A tibble: 4 × 2
#> engine mode
#> <chr> <chr>
#> 1 LiblineaR classification
#> 2 LiblineaR regression
#> 3 kernlab classification
#> 4 kernlab regression
svm_linear(mode = "classification")
#> Linear Support Vector Machine Model Specification (classification)
#>
#> Computational engine: LiblineaR
#>