selectSVMs: Selects the best hyper-parameters of all the trained SVMs.
In liquidSVM: A Fast and Versatile SVM Package

Description Usage Arguments Details Value Documentation for command-line parameters of svm-select See Also

Should only be used by experts! This selects for every task and cell the best hyper-parameter based on the validation errors in the folds. This is saved and will afterwards be used in the evaluation of the decision functions.

1 2	selectSVMs(model, command.args = NULL, ..., d = NULL, warn.suboptimal = getOption("liquidSVM.warn.suboptimal", TRUE))

`model`	the `svm`-model
`command.args`	further arguments aranged in a list, corresponding to the arguments of the command line interface to `svm-select`, e.g. `list(d=2,R=0)` is equivalent to `svm-select -d 2 -R 0`. See command-args for details.
`...`	parameters passed to selection phase e.g. `retrain_method="select_on_entire_train_set"`
`d`	level of display information
`warn.suboptimal`	if TRUE this will issue a warning if the boundary of the hyper-parameter grid was hit too many times. The default can be changed by setting `options(liquidSVM.warn.suboptimal=FALSE)`.

Some learning scenarios have to perform several selection runs: for instance in quantile regression for every quantile. This is done by specifying weight_number ranging from 1 to the number of quantiles.

See command-args for details.

a table giving training and validation errors and more internal statistic for all the SVMs that were selected. This is also recorded in model$select_errors.

The following parameters can be used as well:

h=[<level>]

Displays all help messages.

Meaning of specific values:
<level> = 0 => short help messages
<level> = 1 => detailed help messages

Allowed values:
<level>: 0 or 1

Default values:
<level> = 0
N=c(<class>,<constraint>)

Replaces the best validation error in the search for the best hyper-parameter
combination by an NPL criterion, in which the best detection rate is searched
for given the false alarm constraint <constraint> on class <class>.

Allowed values:
<class>: -1 or 1
<constraint>: float between 0.0 and 1.0

Default values:
Option is deactivated.
R=<method>

Selects the method that produces decision functions from the different folds.

Meaning of specific values:
<method> = 0 => select for best average validation error
<method> = 1 => on each fold select for best validation error

Allowed values:
<method>: integer between 0 and 1

Default values:
<method> = 1
W=<number>

Restrict the search for the best hyper-parameters to weights with the number
<number>.

Meaning of specific values:
<number> = 0 => all weights are considered.

Default values:
<number> = 0