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:

Outputs a message during train.

message_predict:

Outputs a message during predict.

warning_train:

Signals a warning during train.

warning_predict:

Signals a warning during predict.

error_train:

Raises an exception during train.

error_predict:

Raises an exception during predict.

segfault_train:

Provokes a segfault during train.

segfault_predict:

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 parameter. Ignored.

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

LearnerClassifDebug

Format

R6::R6Class inheriting from LearnerClassif.

Examples

learner = LearnerClassifDebug$new() learner$param_set$values = list(message_train = TRUE, save_tasks = TRUE) # this should signal a message task = mlr_tasks$get("iris") learner$train(task)
#> Message from classif.debug->train()
learner$predict(task)
#> <PredictionClassif> for 150 observations: #> row_id truth response #> 1: 1 setosa versicolor #> 2: 2 setosa versicolor #> 3: 3 setosa versicolor #> --- #> 148: 148 virginica versicolor #> 149: 149 virginica versicolor #> 150: 150 virginica versicolor
# task_train and task_predict are the input tasks for train() and predict() names(learner$model)
#> [1] "response" "task_train" "task_predict"