Classification L1 and L2 Penalized Learner

Penalized (L1 and L2) Logistic Regression. Calls penalized::penalized() from: penalized.

Details

The penalized and unpenalized arguments in the learner are implemented slightly differently than in penalized::penalized(). Here, there is no parameter for penalized but instead it is assumed that every variable is penalized unless stated in the unpenalized parameter.

Initial parameter values

• trace is set to "FALSE" to disable printing output during model training.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.penalized")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “logical”, “integer”, “numeric”, “factor”, “ordered”

• Required Packages: mlr3, penalized

Parameters

Id	Type	Default	Levels	Range
epsilon	numeric	1e-10		\([0, \infty)\)
fusedl	logical	FALSE	TRUE, FALSE	-
lambda1	numeric	0		\([0, \infty)\)
lambda2	numeric	0		\([0, \infty)\)
maxiter	integer	-		\([1, \infty)\)
positive	untyped	FALSE		-
standardize	logical	FALSE	TRUE, FALSE	-
startbeta	untyped	-		-
startgamma	untyped	-		-
steps	untyped	1L		-
trace	logical	TRUE	TRUE, FALSE	-
unpenalized	untyped	-		-

References

Goeman, J J (2010). “L1 penalized estimation in the Cox proportional hazards model.” Biometrical journal, 52(1), 70–84.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

annanzrv

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifPenalized

Methods

Public methods

• LearnerClassifPenalized$new()

• LearnerClassifPenalized$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$configure()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$selected_features()
• mlr3::Learner$train()
• mlr3::LearnerClassif$predict_newdata_fast()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifPenalized$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifPenalized$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner
learner = lrn("classif.penalized")
print(learner)
#> 
#> ── <LearnerClassifPenalized> (classif.penalized): Penalized Logistic Regression 
#> • Model: -
#> • Parameters: trace=FALSE
#> • Packages: mlr3 and penalized
#> • Predict Types: [response] and prob
#> • Feature Types: logical, integer, numeric, factor, and ordered
#> • Encapsulation: none (fallback: -)
#> • Properties: twoclass
#> • Other settings: use_weights = 'error'

# Define a Task
task = tsk("sonar")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> Penalized logistic regression object
#> Model failed to converge


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2898551