Classification Learner

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 implemented in the Github package https://github.com/mlr-org/mlr3extralearners.

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

Super class

Learner -> LearnerClassif

Methods

Public methods

• LearnerClassif$new()

• LearnerClassif$predict_newdata_fast()

• LearnerClassif$clone()

Inherited methods

• Learner$base_learner()
• Learner$configure()
• Learner$encapsulate()
• Learner$format()
• Learner$help()
• Learner$predict()
• Learner$predict_newdata()
• Learner$print()
• Learner$reset()
• Learner$selected_features()
• Learner$train()

---

LearnerClassif$new()

Creates a new instance of this R6 class.

Usage

LearnerClassif$new(
  id,
  task_type = "classif",
  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.

task_type

(character(1))
Type of task, e.g. "regr" or "classif". Must be an element of mlr_reflections$task_types$type.

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().

---

LearnerClassif$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. The learner must be trained before calling this method.

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.

---

LearnerClassif$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', predict_raw = 'FALSE'

# 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