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:
Probability to output a message during train.
Probability to output a message during predict.
Probability to signal a warning during train.
Probability to signal a warning during predict.
Probability to raises an exception during train.
Probability to raise an exception during predict.
Probability to provokes a segfault during train.
Probability to provokes a segfault during predict.
Ratio of predictions which will be NA.
Saves input task in
model slot during training and prediction.
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!
Creates a new instance of this R6 class.
The objects of this class are cloneable with this method.
LearnerClassifDebug$clone(deep = FALSE)
Whether to make a deep clone.
learner = lrn("classif.debug") learner$param_set$values = list(message_train = 1, save_tasks = TRUE) # this should signal a message task = tsk("iris") learner$train(task)#>learner$predict(task)#> <PredictionClassif> for 150 observations: #> row_id truth response #> 1 setosa versicolor #> 2 setosa versicolor #> 3 setosa versicolor #> --- #> 148 virginica versicolor #> 149 virginica versicolor #> 150 virginica versicolor# task_train and task_predict are the input tasks for train() and predict() names(learner$model)#>  "response" "task_train" "task_predict"