Base class for filters. Predefined filters are stored in the dictionary mlr_filters. A Filter calculates a score for each feature of a task. Important features get a large value and unimportant features get a small value. Note that filter scores may also be negative.
id(character(1))
Identifier of the object.
Used in tables, plot and text output.
task_type(character(1))
Task type, e.g. "classif" or "regr".
For a complete list of possible task types (depending on the loaded packages),
see mlr_reflections$task_types$type.
task_properties(character())
mlr3::Tasktask properties.
param_set(paradox::ParamSet) Set of hyperparameters.
feature_types(character())
Feature types of the filter.
packages(character())
Packages which this filter is relying on.
man(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object.
Defaults to NA, but can be set by child classes.
scoresStores the calculated filter score values as named numeric vector.
The vector is sorted in decreasing order with possible NA values
last. Tied values (this includes NA values) appear in a random,
non-deterministic order.
new()Create a Filter object.
Filter$new( id, task_type, task_properties = character(), param_set = ParamSet$new(), feature_types = character(), packages = character(), man = NA_character_ )
id(character(1))
Identifier for the filter.
task_type(character())
Types of the task the filter can operator on. E.g., "classif" or
"regr".
task_properties(character())
Required task properties, see mlr3::Task.
Must be a subset of
mlr_reflections$task_properties.
param_set(paradox::ParamSet) Set of hyperparameters.
feature_types(character())
Feature types the filter operates on.
Must be a subset of
mlr_reflections$task_feature_types.
packages(character())
Set of required packages.
Note that these packages will be loaded via requireNamespace(), and
are not attached.
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
$help().
format()Format helper for Filter class
Filter$format()
print()Printer for Filter class
Filter$print()
help()Opens the corresponding help page referenced by field $man.
Filter$help()
calculate()Calculates the filter score values for the provided mlr3::Task and
stores them in field scores. nfeat determines the minimum number of
features to score (see details), and defaults to the number
of features in task. Loads required packages and then calls
private$.calculate() of the respective subclass.
If the task has no rows, each feature gets the score NA.
Filter$calculate(task, nfeat = NULL)
task(mlr3::Task) mlr3::Task to calculate the filter scores for.
nfeat(integer())
THe minimum number of features to calculate filter scores for.
clone()The objects of this class are cloneable with this method.
Filter$clone(deep = FALSE)
deepWhether to make a deep clone.
Some features support partial scoring of the feature set:
If nfeat is not NULL, only the best nfeat features are guaranteed to
get a score. Additional features may be ignored for computational reasons,
and then get a score value of NA.
Other Filter:
mlr_filters_anova,
mlr_filters_auc,
mlr_filters_carscore,
mlr_filters_cmim,
mlr_filters_correlation,
mlr_filters_disr,
mlr_filters_find_correlation,
mlr_filters_importance,
mlr_filters_information_gain,
mlr_filters_jmim,
mlr_filters_jmi,
mlr_filters_kruskal_test,
mlr_filters_mim,
mlr_filters_mrmr,
mlr_filters_njmim,
mlr_filters_performance,
mlr_filters_permutation,
mlr_filters_variance,
mlr_filters