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Classification Learner

Source: R/LearnerClassif.R
LearnerClassif.Rd

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.

See also

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

Inherited methods

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(),
  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.

  • "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.

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 predict_newdata_fast()

Predicts outcomes for new data in newdata using the model fitted during $train(). This method is faster than $predict_newdata() as it skips assertions, type conversions, encapsulation, and logging.

Unlike $predict_newdata(), this method does not return a Prediction object. Instead, it returns a list with either a "response" or "prob" element, depending on the prediction type.

Note that state$predict_time and state$log will remain empty after using this method. Some learners may not support this method and may fail when it is called. Prefer $predict_newdata() unless performance is critical.

If the model was trained via resample() or benchmark(), you must pass the associated task object stored in the corresponding ResampleResult or BenchmarkResult.

Usage

LearnerClassif$predict_newdata_fast(newdata, task = NULL)

Arguments

newdata

data.table::data.table()
New data to predict on.

task

(Task).

Returns

list() with elements "response" or "prob" depending on the predict type.

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.

Examples

# get all classification learners from mlr_learners:
lrns = mlr_learners$mget(mlr_learners$keys("^classif"))
names(lrns)
#> [1] "classif.debug"       "classif.featureless" "classif.rpart"      

# get a specific learner from mlr_learners:
lrn = lrn("classif.rpart")
print(lrn)
#> 
#> ── <LearnerClassifRpart> (classif.rpart): Classification Tree ──────────────────
#> • Model: -
#> • Parameters: xval=0
#> • Packages: mlr3 and rpart
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: importance, missings, multiclass, selected_features, twoclass,
#> and weights
#> • Other settings: use_weights = 'use'

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

# predict on new observations:
lrn$predict(task, 201:344)$confusion
#>            truth
#> response    Adelie Chinstrap Gentoo
#>   Adelie         0        62      1
#>   Chinstrap      0         0      0
#>   Gentoo         0         6     75