mlr3 (version 0.3.0)

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.

save_tasks:

Saves input task in model slot during training and prediction.

x:

Numeric tuning parameter. Has no effect.

Note that segfaults may not be triggered on your operating system. Also note that if they work, 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")

Super classes

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

Methods

Public methods

• LearnerClassifDebug$new()

• LearnerClassifDebug$clone()

• 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

Dictionary of Learners: mlr_learners

as.data.table(mlr_learners) for a complete table of all (also dynamically created) Learner implementations.

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("iris")
learner$train(task)
learner$predict(task)

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