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:

error_predict:

Probability to raise an exception during predict.

error_train:

Probability to raises an exception during train.

message_predict:

Probability to output a message during predict.

message_train:

Probability to output a message during train.

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.

Saves input task in model slot during training and prediction.

segfault_predict:

Probability to provokes a segfault during predict.

segfault_train:

Probability to provokes a segfault during train.

Number of threads to use. Has no effect.

warning_predict:

Probability to signal a warning during predict.

warning_train:

Probability to signal a warning during train.

x:

Numeric tuning parameter. Has no effect.

Note that segfaults may not be triggered reliably 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():

### Method clone()

The objects of this class are cloneable with this method.

LearnerClassifDebug$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples learner = lrn("classif.debug") learner$param_set$values = list(message_train = 1, save_tasks = TRUE) # this should signal a message task = tsk("penguins") learner$train(task)
#> Message from classif.debug->train()learner$predict(task) #> <PredictionClassif> for 344 observations: #> row_ids truth response #> 1 Adelie Gentoo #> 2 Adelie Gentoo #> 3 Adelie Gentoo #> --- #> 342 Chinstrap Gentoo #> 343 Chinstrap Gentoo #> 344 Chinstrap Gentoo # task_train and task_predict are the input tasks for train() and predict() names(learner$model)
#> [1] "response"     "pid"          "task_train"   "task_predict"