The `FSelector“ implements the optimization algorithm.
Details
FSelector
is an abstract base class that implements the base functionality each fselector must provide.
Resources
There are several sections about feature selection in the mlr3book.
Learn more about fselectors.
The gallery features a collection of case studies and demos about optimization.
Utilize the built-in feature importance of models with Recursive Feature Elimination.
Run a feature selection with Shadow Variable Search.
Active bindings
param_set
paradox::ParamSet
Set of control parameters.properties
(
character()
)
Set of properties of the fselector. Must be a subset ofmlr_reflections$fselect_properties
.packages
(
character()
)
Set of required packages. Note that these packages will be loaded viarequireNamespace()
, and are not attached.label
(
character(1)
)
Label for this object. Can be used in tables, plot and text output instead of the ID.man
(
character(1)
)
String in the format[pkg]::[topic]
pointing to a manual page for this object. The referenced help package can be opened via method$help()
.
Methods
Method new()
Creates a new instance of this R6 class.
Arguments
id
(
character(1)
)
Identifier for the new instance.param_set
paradox::ParamSet
Set of control parameters.properties
(
character()
)
Set of properties of the fselector. Must be a subset ofmlr_reflections$fselect_properties
.packages
(
character()
)
Set of required packages. Note that these packages will be loaded viarequireNamespace()
, and are not attached.label
(
character(1)
)
Label for this object. Can be used in tables, plot and text output instead of the ID.man
(
character(1)
)
String in the format[pkg]::[topic]
pointing to a manual page for this object. The referenced help package can be opened via method$help()
.