mlr3 (version 0.12.0)

mlr_learners_classif.debug: Classification Learner for Debugging

Description

A simple LearnerClassif used primarily in the unit tests and for debugging purposes. If no hyperparameter is set, it simply constantly predicts a randomly selected label. The following hyperparameters trigger the following actions:

message_train:

Probability to output a message during train.

message_predict:

Probability to output a message during predict.

warning_train:

Probability to signal a warning during train.

warning_predict:

Probability to signal a warning during predict.

error_train:

Probability to raises an exception during train.

error_predict:

Probability to raise an exception during predict.

segfault_train:

Probability to provokes a segfault during train.

segfault_predict:

Probability to provokes a segfault during predict.

predict_missing

Ratio of predictions which will be NA.

predict_missing_type

To to encode missingness. “na” will insert NA values, “omit” will just return fewer predictions than requested.

save_tasks:

Saves input task in model slot during training and prediction.

threads:

Number of threads to use. Has no effect.

x:

Numeric tuning parameter. Has no effect.

Note that segfaults may not be triggered on your operating system. Also note that if they work as intended, they will tear down your R session immediately!

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("classif.featureless")
lrn("classif.featureless")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

• Required Packages: -

Parameters

Id	Type	Default	Range	Levels
error_predict	numeric	0	\([0, 1]\)	-
error_train	numeric	0	\([0, 1]\)	-
message_predict	numeric	0	\([0, 1]\)	-
message_train	numeric	0	\([0, 1]\)	-
predict_missing	numeric	0	\([0, 1]\)	-
predict_missing_type	character	na	\((-\infty, \infty)\)	na, omit
save_tasks	logical	FALSE	\((-\infty, \infty)\)	TRUE, FALSE
segfault_predict	numeric	0	\([0, 1]\)	-
segfault_train	numeric	0	\([0, 1]\)	-
threads	integer	-	\([1, \infty)\)	-
warning_predict	numeric	0	\([0, 1]\)	-
warning_train	numeric	0	\([0, 1]\)	-
x	numeric	-	\([0, 1]\)	-

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifDebug

Methods

Public methods

• LearnerClassifDebug$new()

• LearnerClassifDebug$clone()

• 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

LearnerClassifDebug$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifDebug$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/learners.html

• 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).

• Extension packages for additional task types:

  • mlr3proba for probabilistic supervised regression and survival analysis.

  • mlr3cluster for unsupervised clustering.

Other Learner: LearnerClassif, LearnerRegr, Learner, mlr_learners_classif.featureless, mlr_learners_classif.rpart, mlr_learners_regr.featureless, mlr_learners_regr.rpart, mlr_learners

Examples

# NOT RUN {
learner = lrn("classif.debug")
learner$param_set$values = list(message_train = 1, save_tasks = TRUE)

# this should signal a message
task = tsk("penguins")
learner$train(task)
learner$predict(task)

# task_train and task_predict are the input tasks for train() and predict()
names(learner$model)
# }