Linear support vector machines (SVMs) via LiblineaR
Source:R/svm_linear_LiblineaR.R
details_svm_linear_LiblineaR.RdLiblineaR::LiblineaR() 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: 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).
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 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.
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.