This Learner specializes Learner for density estimation problems:
task_type
is set to"dens"
Creates Predictions of class PredictionDens.
Possible values for
predict_types
are:"pdf"
: Evaluates estimated probability density function for each value in the test set."cdf"
: Evaluates estimated cumulative distribution function for each value in the test set.
See also
Other Learner:
LearnerSurv
Super class
mlr3::Learner
-> LearnerDens
Methods
Method new()
Creates a new instance of this R6 class.
Usage
LearnerDens$new(
id,
param_set = ps(),
predict_types = "cdf",
feature_types = character(),
properties = character(),
data_formats = "data.table",
packages = character(),
label = NA_character_,
man = NA_character_
)
Arguments
id
(
character(1)
)
Identifier for the new instance.param_set
(paradox::ParamSet)
Set of hyperparameters.predict_types
(
character()
)
Supported predict types. Must be a subset ofmlr_reflections$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).
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$help()
.
Examples
library(mlr3)
# get all density learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^dens"))
names(lrns)
#> [1] "dens.hist" "dens.kde"
# get a specific learner from mlr_learners:
mlr_learners$get("dens.hist")
#> <LearnerDensHistogram:dens.hist>: Histogram Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, distr6
#> * Predict Types: [pdf], cdf, distr
#> * Feature Types: integer, numeric
#> * Properties: -
lrn("dens.hist")
#> <LearnerDensHistogram:dens.hist>: Histogram Density Estimator
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, distr6
#> * Predict Types: [pdf], cdf, distr
#> * Feature Types: integer, numeric
#> * Properties: -