LearnerRegr {mlr3} | R Documentation |
Regression Learner
Description
This Learner specializes Learner for regression problems:
-
task_type
is set to"regr"
. Creates Predictions of class PredictionRegr.
Possible values for
predict_types
are:-
"response"
: Predicts a numeric response for each observation in the test set. -
"se"
: Predicts the standard error for each value of response for each observation in the test set. -
"distr"
: Probability distribution asVectorDistribution
object (requires packagedistr6
, available via repository https://raphaels1.r-universe.dev).
-
Predefined learners can be found in the dictionary mlr_learners. Essential regression learners can be found in this dictionary after loading mlr3learners. Additional learners are implement in the Github package https://github.com/mlr-org/mlr3extralearners.
Super class
mlr3::Learner
-> LearnerRegr
Methods
Public methods
Inherited methods
Method new()
Creates a new instance of this R6 class.
Usage
LearnerRegr$new( id, param_set = ps(), predict_types = "response", feature_types = character(), properties = character(), data_formats = "data.table", packages = character(), label = NA_character_, man = NA_character_ )
Arguments
id
(
character(1)
)
Identifier for the new instance.param_set
(paradox::ParamSet)
Set of hyperparameters.predict_types
(
character()
)
Supported predict types. Must be a subset ofmlr_reflections$learner_predict_types
.feature_types
(
character()
)
Feature types the learner operates on. Must be a subset ofmlr_reflections$task_feature_types
.properties
(
character()
)
Set of properties of the Learner. Must be a subset ofmlr_reflections$learner_properties
. The following properties are currently standardized and understood by learners in mlr3:-
"missings"
: The learner can handle missing values in the data. -
"weights"
: The learner supports observation weights. -
"importance"
: The learner supports extraction of importance scores, i.e. comes with an$importance()
extractor function (see section on optional extractors in Learner). -
"selected_features"
: The learner supports extraction of the set of selected features, i.e. comes with a$selected_features()
extractor function (see section on optional extractors in Learner). -
"oob_error"
: The learner supports extraction of estimated out of bag error, i.e. comes with aoob_error()
extractor function (see section on optional extractors in Learner). -
"validation"
: The learner can use a validation task during training. -
"internal_tuning"
: The learner is able to internally optimize hyperparameters (those are also tagged with"internal_tuning"
). -
"marshal"
: To save learners with this property, you need to call$marshal()
first. If a learner is in a marshaled state, you call first need to call$unmarshal()
to use its model, e.g. for prediction.
-
data_formats
(
character()
)
Set of supported data formats which can be processed during$train()
and$predict()
, e.g."data.table"
.packages
(
character()
)
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand viarequireNamespace()
.label
(
character(1)
)
Label for the new instance.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$help()
.
Method clone()
The objects of this class are cloneable with this method.
Usage
LearnerRegr$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
See Also
Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
Package mlr3learners for a solid collection of essential learners.
Package mlr3extralearners for more learners.
-
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages). -
mlr3pipelines to combine learners with pre- and postprocessing steps.
Package mlr3viz for some generic visualizations.
Extension packages for additional task types:
-
mlr3proba for probabilistic supervised regression and survival analysis.
-
mlr3cluster for unsupervised clustering.
-
-
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Other Learner:
Learner
,
LearnerClassif
,
mlr_learners
,
mlr_learners_classif.debug
,
mlr_learners_classif.featureless
,
mlr_learners_classif.rpart
,
mlr_learners_regr.debug
,
mlr_learners_regr.featureless
,
mlr_learners_regr.rpart
Examples
# get all regression learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^regr"))
names(lrns)
# get a specific learner from mlr_learners:
mlr_learners$get("regr.rpart")
lrn("classif.featureless")