# Linear support vector machines (SVMs) via kernlab

Source:`R/svm_linear_kernlab.R`

`details_svm_linear_kernlab.Rd`

`kernlab::ksvm()`

fits a support vector machine model. For classification,
the model tries to maximize the width of the margin between classes.
For regression, the model optimizes a robust loss function that is only
affected by very large model residuals.

## Details

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: 0.1)

### Translation from parsnip to the original package (regression)

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

```
## Linear Support Vector Machine Model Specification (regression)
##
## Main Arguments:
## cost = double(1)
## margin = double(1)
##
## Computational engine: kernlab
##
## Model fit template:
## kernlab::ksvm(x = missing_arg(), data = missing_arg(), C = double(1),
## epsilon = double(1), kernel = "vanilladot")
```

### Translation from parsnip to the original package (classification)

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

```
## Linear Support Vector Machine Model Specification (classification)
##
## Main Arguments:
## cost = double(1)
##
## Computational engine: kernlab
##
## Model fit template:
## kernlab::ksvm(x = missing_arg(), data = missing_arg(), C = double(1),
## kernel = "vanilladot", prob.model = TRUE)
```

The `margin`

parameter does not apply to classification models.

Note that the `"kernlab"`

engine does not naturally estimate class
probabilities. To produce them, the decision values of the model are
converted to probabilities using Platt scaling. This method fits an
additional model on top of the SVM model. When fitting the Platt scaling
model, random numbers are used that are not reproducible or controlled
by R’s random number stream.

### 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 `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.

### Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.

### Examples

The “Fitting and Predicting with parsnip” article contains
examples
for `svm_linear()`

with the `"kernlab"`

engine.

### References

Lin, HT, and R Weng. “A Note on Platt’s Probabilistic Outputs for Support Vector Machines”

Karatzoglou, A, Smola, A, Hornik, K, and A Zeileis. 2004. “kernlab - An S4 Package for Kernel Methods in R.”,

*Journal of Statistical Software*.Kuhn, M, and K Johnson. 2013.

*Applied Predictive Modeling*. Springer.