details_svm_linear_LiblineaR | R Documentation |
LiblineaR::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.
For this engine, there are multiple modes: classification and regression
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).
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)
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).
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.
The underlying model implementation does not allow for case weights.
The “Fitting and Predicting with parsnip” article contains
examples
for svm_linear()
with the "LiblineaR"
engine.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.