Setup RadialSVM Hyperparameters — setup

Setup hyperparameters for RadialSVM training.

Usage

setup_RadialSVM(cost = 1, gamma = 0.01, ifw = FALSE)

Arguments

cost: (Tunable) Numeric: Cost of constraints violation.
gamma: (Tunable) Numeric: Kernel coefficient.
ifw: Logical: If TRUE, use Inverse Frequency Weighting in classification.

Value

RadialSVMHyperparameters object.

Details

Get more information from e1071::svm.

Author

EDG

Examples

radial_svm_hyperparams <- setup_RadialSVM(cost = 10, gamma = 0.1, ifw = TRUE)
radial_svm_hyperparams
#> <RadialSVMHyperparameters>
#>         hyperparameters: 
#>                          kernel: <chr> radial
#>                            cost: <nmr> 10.00
#>                           gamma: <nmr> 0.10
#>                             ifw: <lgc> TRUE
#> tunable_hyperparameters: <chr> cost, gamma, ifw
#>   fixed_hyperparameters: <chr> 
#>                   tuned: <int> -1
#>               resampled: <int> 0
#>               n_workers: <int> 1
#> 
#>   No search values defined for tunable hyperparameters.