mlr3 (version 0.20.2)

LearnerClassif: Classification Learner

Description

This Learner specializes Learner for classification problems:

• task_type is set to "classif".

• Creates Predictions of class PredictionClassif.

• Possible values for predict_types are:

  • "response": Predicts a class label for each observation in the test set.

  • "prob": Predicts the posterior probability for each class for each observation in the test set.

• Additional learner properties include:

  • "twoclass": The learner works on binary classification problems.

  • "multiclass": The learner works on multiclass classification problems.

Predefined learners can be found in the dictionary mlr_learners. Essential classification 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 -> LearnerClassif

Methods

Public methods

• LearnerClassif$new()

• LearnerClassif$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassif$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 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).

• "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 via requireNamespace().

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

LearnerClassif$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.

• Dictionary of Learners: mlr_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, LearnerRegr, 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 classification learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^classif"))
names(lrns)

# get a specific learner from mlr_learners:
lrn = lrn("classif.rpart")
print(lrn)

# train the learner:
task = tsk("penguins")
lrn$train(task, 1:200)

# predict on new observations:
lrn$predict(task, 201:344)$confusion