LearnerClassif function

Classification Learner

This Learner specializes Learner for classification problems:

• task_type is set to "classif".

• Creates Prediction s 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 list("mlr3learners"). Additional learners are implement in the Github package https://github.com/mlr-org/mlr3extralearners.

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

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners

• Package list("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).

• list("mlr3pipelines") to combine learners with pre- and postprocessing steps.

• Package list("mlr3viz") for some generic visualizations.

• Extension packages for additional task types:

  • list("mlr3proba") for probabilistic supervised regression and survival analysis.
  • list("mlr3cluster") for unsupervised clustering.

• list("mlr3tuning") for tuning of hyperparameters, list("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

Super class

mlr3::Learner -> LearnerClassif

Methods

Public methods

• list("LearnerClassif$new()")

• list("LearnerClassif$clone()")

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,
  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 [list("mlr3")](https://CRAN.R-project.org/package=mlr3):
   
    * `"missings"`: The learner can handle missing values in the data.
    * `"weights"`: The learner supports observation weights.
    * `"offset"`: The learner can incorporate offset values to adjust predictions.
    * `"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.
    * `"hotstart_forward"`: The learner supports to hotstart a model forward.
    * `"hotstart_backward"`: The learner supports hotstarting a model backward.
    * `"featureless": The learner does not use features.
  

• data_formats: (character())

   Deprecated: ignored, and will be removed in the future.
  

• 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.