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.
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.
Number of threads to use. Has no effect.
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!
This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function
Task type: “classif”
Predict Types: “response”, “prob”
Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”
Required Packages: -
|predict_missing_type||character||na||(-Inf, Inf)||na, omit|
|save_tasks||logical||FALSE||(-Inf, Inf)||TRUE, FALSE|
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.
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:
1 2 3 4 5 6 7 8 9 10
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