mlr_learners_classif.abess: Classification Abess Learner

In mlr-org/mlr3extralearners: Extra Learners For mlr3

Classification Abess Learner

Description

Adaptive best-subset selection for classification. Calls abess::abess() from abess.

Dictionary

This Learner can be instantiated via lrn():

lrn("classif.abess")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

• Feature Types: “integer”, “numeric”

• Required Packages: mlr3, abess

Parameters

Id	Type	Default	Levels	Range
family	character	-	binomial, multinomial, ordinal	-
tune.path	character	sequence	sequence, gsection	-
tune.type	character	gic	gic, aic, bic, ebic, cv	-
normalize	integer	NULL		(-\infty, \infty)
support.size	untyped	NULL		-
c.max	integer	2		[1, \infty)
gs.range	untyped	NULL		-
lambda	numeric	0		[0, \infty)
always.include	untyped	NULL		-
group.index	untyped	NULL		-
init.active.set	untyped	NULL		-
splicing.type	integer	2		[1, 2]
max.splicing.iter	integer	20		[1, \infty)
screening.num	integer	NULL		[0, \infty)
important.search	integer	NULL		[0, \infty)
warm.start	logical	TRUE	TRUE, FALSE	-
nfolds	integer	5		(-\infty, \infty)
foldid	untyped	NULL		-
cov.update	logical	FALSE	TRUE, FALSE	-
newton	character	exact	exact, approx	-
newton.thresh	numeric	1e-06		[0, \infty)
max.newton.iter	integer	NULL		[1, \infty)
early.stop	logical	FALSE	TRUE, FALSE	-
ic.scale	numeric	1		[0, \infty)
num.threads	integer	0		[0, \infty)
seed	integer	0		(-\infty, \infty)

Initial parameter values

• num.threads: This parameter is initialized to 1 (default is 0) to avoid conflicts with the mlr3 parallelization.

• family: Depends on the task type, if the parameter family is NULL, it is set to "binomial" for binary classification tasks and to "multinomial" for multiclass classification problems.

Super classes

mlr3::Learner -> mlr3::LearnerClassif -> LearnerClassifAbess

Methods

Public methods

• LearnerClassifAbess$new()

• LearnerClassifAbess$selected_features()

• LearnerClassifAbess$clone()

Inherited methods

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

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerClassifAbess$new()

---

Method selected_features()

Extract the name of selected features from the model by abess::extract().

Usage

LearnerClassifAbess$selected_features()

Returns

The names of selected features

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifAbess$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Author(s)

abess-team

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.

Examples

# Define the Learner
learner = mlr3::lrn("classif.abess")
print(learner)

# Define a Task
task = mlr3::tsk("sonar")

# Create train and test set
ids = mlr3::partition(task)

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

print(learner$model)


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

# Score the predictions
predictions$score()

mlr-org/mlr3extralearners documentation built on Nov. 11, 2024, 11:11 a.m.