Regression Learner
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 as distr6::VectorDistribution object (requires package distr6).
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.
mlr3::Learner -> LearnerRegr
new()
Creates a new instance of this R6 class.
LearnerRegr$new( id, param_set = ParamSet$new(), predict_types = "response", feature_types = character(), properties = character(), data_formats = "data.table", packages = character(), man = NA_character_ )
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 of mlr_reflections$learner_predict_types.
feature_types(character())
Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.
properties(character())
Set of properties of the Learner.
Must be a subset of mlr_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 a oob_error() extractor function (see section on optional extractors in Learner).
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 via requireNamespace().
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 $help().
clone()
The objects of this class are cloneable with this method.
LearnerRegr$clone(deep = FALSE)
deepWhether to make a deep clone.
# 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")
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