Classification Abess Learner

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.

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

abess-team

Super classes

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

Methods

Public methods

• LearnerClassifAbess$new()

• LearnerClassifAbess$selected_features()

• LearnerClassifAbess$clone()

Inherited methods

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

---

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

---

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.

Examples

# Define the Learner
learner = mlr3::lrn("classif.abess")
print(learner)
#> 
#> ── <LearnerClassifAbess> (classif.abess): Fast Best Subset Selection for Classif
#> • Model: -
#> • Parameters: num.threads=1
#> • Packages: mlr3 and abess
#> • Predict Types: [response] and prob
#> • Feature Types: integer and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: multiclass, selected_features, twoclass, and weights
#> • Other settings: use_weights = 'use'

# 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)
#> Call:
#> abess.default(x = x, y = y, family = "binomial", num.threads = 1L)
#> 
#>    support.size      dev      GIC
#> 1             0 96.34386 192.6877
#> 2             1 77.54259 161.6208
#> 3             2 73.80409 160.6793
#> 4             3 66.39610 152.3989
#> 5             4 62.97998 152.1023
#> 6             5 60.30137 153.2806
#> 7             6 57.27219 153.7579
#> 8             7 54.24646 154.2420
#> 9             8 52.23219 156.7490
#> 10            9 49.32739 157.4750
#> 11           10 46.16553 157.6869
#> 12           11 42.78770 157.4668
#> 13           12 40.01961 158.4662
#> 14           13 36.75307 158.4687
#> 15           14 34.53900 160.5761
#> 16           15 32.77788 163.5895
#> 17           16 29.64610 163.8615
#> 18           17 27.30728 165.7194
#> 19           18 26.04582 169.7321
#> 20           19 25.22697 174.6300
#> 21           20 24.15616 179.0240
#> 22           21 23.50762 184.2625


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

# Score the predictions
predictions$score()
#> classif.ce 
#>  0.2463768